data, coding

What does a data scientist look like? My journey to becoming a data scientist and a mother

data, coding

By Karin Sasaki, Senior Consultant in Data Science, Ekimetrics

Career paths are rarely straightforward, are they?

When I was studying, I wanted to become an applied academic, using data to solve problems. I completed my PhD in Biomathematics, which led to my first job: working for the European Molecular Biology Laboratory. I was helping to create mathematical models of biological systems.

Six years later, and I’ve pivoted a little from the original life plan. I’m now working as a data scientist for Ekimetrics, where we use data to help businesses better understand consumers, their marketing, or to improve their products and services.

It was a leap of faith to leave academia, but I had found a true passion in data; I love the different ways it can be used. And so, maybe unsurprisingly, I landed on data science as a career path. And so, the research began, and I read deeply and widely, looking for areas that interested me and ways to get a foothold in the industry.

Read. That’s one key piece of advice I’d give to anyone worried about switching careers or trying something new. Really throw yourself into the literature around a subject and spend free time learning more about it. Most things aren’t as terrifying or as difficult to understand as you might expect!

For me, I found a really great data science community online – but I’d say the same is true across a great many industries. People write really helpful how-to articles, and they’ll offer to help you find the answers when you need them.

Connecting with people and networking is another great way to find out whether you’re comfortable in a certain field.

The journey rather than the industry

I’ve always been ‘subject agnostic’ and more interested in the process of finding an answer, rather than a specific sector or industry.

So, to me, a company like Ekimetrics with many different clients and types of businesses is fascinating. I love being able to use my background to take data sets and translate them into something businesses can understand and feel comfortable with.

Marketing effectiveness is particularly interesting to me because there are so many different data types and customer interests to analyse. At the moment, I’m involved in a couple of projects that are helping companies understand customers and their behavior better. In turn, this is helping us outline the most valuable areas of each business, so we can see how it can better serve its customers.

It’s rewarding to see our work have a direct and measurable impact on the success of a business. And it’s brilliant to feel we’re helping in a tangible way.

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Choosing to become a mother

Of course, when you love your career, motherhood isn’t always a straightforward choice to navigate. The fear of losing your career can be a daunting. I am sure countless women have struggled with this and I know it still influences women’s choices around the world.

I’m very grateful to my family, friends, and work colleagues, both male and female, who have supported me. I’m now blessed to have a son.

When I was pregnant – and then again when raising a small child – I noticed the support of those around me the most. At work, my colleagues saw my output wasn’t diminished, I was just working in different patterns.

Regardless of how I got the work done, they still trusted that projects would be finished on time and to a high standard. Having this trust motivated me to get the job done well. My husband has been wonderful and taken on more around the house whenever I needed to interview for roles or work particularly hard. And having my extended family to help gives me more time to develop my career.

Ultimately, while it should always be a woman’s choice to become a mother, it isn’t always possible to do this alone. Parenting itself is often a full-time job and so support is vital to continue to thrive and reach your potential in the workplace.

Diversity within Science, Technology, Engineering and Maths (STEM) careers

Obviously, my thoughts on juggling children and work are probably less relevant to much younger girls and women who are thinking about a STEM career!

In data science, like any industry, there is no one way in. There are many different routes and I am proud to demonstrate that!

To anyone who is, and is feeling unsure, I studied maths because I was interested in it, but I have seen that if your interests change you can change your career plans too. You’re never pigeonholed into something for life. If you want to make a change, go for it!

I did a lot of studying online and there is more support than ever, for example, via e-courses. Plus, being a data scientist doesn’t mean you’ve had to have a particular background in maths. Actually, in line with removing bias, it is good to have a wide number of backgrounds in any particular team. Different viewpoints are welcome.

Having a mix of people in terms of gender, academic and cultural backgrounds, changes the dynamics of a workplace for the better. It means being able to bring your full self to work and not be afraid of expressing yourself. In a workplace like that people feel freer to be and express themselves and that positivity permeates into the work and collaboration.

It also creates better business outcomes. According to the McKinsey study “Why Diversity Matters,” companies in the top quartile for gender-diverse executive suites were 15% more likely to generate above-average profitability compared to the bottom quartile of companies whose executive teams were predominantly white and male.

I hope that women such as myself can continue to break the bias around certain careers and encourage diversity. In doing so, I truly believe we will support much more prosperous societies and do better business.

Karin SasakiAbout the author

Karin is a mathematician with a Ph.D. and five years’ experience in modelling and data analysis in various industry and academic settings. She has worked with a variety of data that has come from molecular biology systems, as well as from operational research, and now marketing. Her specific modelling and analytical skills include low dimensional topology, topological data analysis and machine learning.


female data scientist, woman leading team

The world needs more data scientists

female data scientist, woman leading team

Dr Anya Rumyantseva, Senior Data Scientist at Hitachi Vantara

Data science is often referred to as a ‘dark art’.

As a data scientist myself, I don’t think the field is that mystifying. But for those outside of the profession, there is some lack of awareness of what a data scientist actually does, and what pursuing a career in the field entails.

This can be a real problem – because today, data makes the world go around.

Most companies, regardless of industry, are seeking new ways to leverage the vast amounts of data at their fingertips as a tool to drive efficiencies and transform their business model. But like any tool, data is only useful if it’s in the hands of someone who knows how to use it. It’s easy to forget that digital transformation is as much about people as it is about technology.

The talent deficit 

The UK has been struggling with a skills shortage for some time now. As digital transformation influences every sector, businesses are turning to experts who can help them harness their data. Companies are on the hunt for data engineers, machine learning engineers and data scientists. One study found that in the UK, the demand for people with specialist data skills has more than tripled over the past five years, while another projected the data scientist role will account for 28 per cent of all digital jobs by next year.

It’s a case of supply and demand – but unfortunately, many companies are encountering a sparse talent pool to recruit from. Some estimates even suggest that Europe needs around 346,000 more people trained in data science by 2020. That’s a big gap to fill – and it’s only going to get wider unless the industry takes action.

The data landscape is getting increasingly complex – how much data we’re generating, the types of data and how we’re storing it is changing. To put this in perspective: I’m working on a project right now that uses a petabyte of data. I’m able to work with this huge amount of data because today we have the infrastructure to store it, process it and apply machine learning models. Rewind to the 80s and it would have cost around $600 billion just to store that much data.

Now that we have the tools to work with such large data sets, we’re able to leverage data in exciting new ways. However, this also means we need more people capable of doing so. Considering that IDC forecasts a massive 163 zettabytes of data will be generated by businesses every year by 2025, it’s no wonder UK businesses are worried about a deficit in data specialists.

So, how do we mitigate an impending skills shortage? Well, a good place to start is by changing perceptions of what a data scientist actually is and what they do.

Demystifying the ‘dark arts’

I’ve been a data scientist in Hitachi Vantara’s Solution Engineering team for over two years now. When people ask me what I do, the answer may not be what they expect. My role is to understand the business challenges of our customers, consider potential analytical approaches to solving these challenges and prototype solutions by using advanced analytics, machine learning and deep learning techniques.

In short, I leverage data and mathematical techniques to solve business problems. It’s an exciting field to work in – and can have a significant real-world impact.

As an example, consider the UK rail system. It’s one of the busiest in the world, ferrying thousands of people from point A to B every single day. When you’re a passenger, you probably don’t think about the intricate and nuanced system that keeps your train running. That is, until something goes wrong. Like when a train door gets jammed and is prevented from leaving the station on time. One seemingly minor fault can have a huge knock-on effect further down the line, causing delays and disruption for thousands of passengers.

That’s one real-world problem that I’m trying to help to solve right now. Leveraging data collected from thousands of sensors on the trains themselves and working directly with rail engineers, as a data scientist on the project I bridge the gap between engineering and mathematics, uncovering insights that can drive efficiencies and reduce delays.

Diversity matters

Hopefully now you’ll think of a data scientist as more than just someone who sits behind a computer screen doing equations all day! But the tech sector needs to work hard to build a more inclusive environment where young people – regardless of their background, gender or race – consider data science as an attractive career option.

At Hitachi Vantara, we run a data science internship programme in our London office for talented and intellectually curious young people from diverse backgrounds. Our interns roll up their sleeves and get stuck into analytical projects. They are an important part of the team and their opinions matter. We challenge them to think creatively, asking them to leverage publicly available data to uncover insights into real-world problems – like using data from the Department of Transport to think up new ways to reduce carbon emissions from private and commercial vehicles in the UK. It’s not just a fun thought-experiment – it’s an accurate glimpse into the life of a data scientist.

Data science is a diverse, interesting and constantly evolving field – so it needs people who can think differently, bring new ideas and offer fresh perspectives. If we’re going to tackle the skills shortage, the industry must hold the door open for people from all walks of life.

Anya Rumyantseva, Senior Data Scientist, Hitachi VantaraAbout the author

Anya Rumyantseva is a Senior Data Scientist at Hitachi Vantara. Anya received a Ph.D. degree from the University of Southampton and BS/MS degree in Physics from Lomonosov Moscow State University. Anya is also a fellow of the Nippon Foundation (Japan). Her PhD thesis was focused on using IoT data obtained from marine robotic systems for improving our understanding of phytoplankton blooms and their impact on the global climate. At Hitachi Vantara, Anya is working on projects that use advanced analysis and machine learning techniques to improve business operations in the railway, manufacturing and other industries traditional for Hitachi group. 


Inspirational Woman: Nyala Noë | Data Scientist, Empirisys

Nyala NoeMy name is Nyala Noë and I am a data scientist. I am Dutch, but was born in Germany and grew up in France.

I completed my Masters in Social Psychology at the VU Amsterdam (The Netherlands) in 2014 and my PhD in Computer Science and Informatics from Cardiff University (Wales) in 2018. Since 2018, I have been working as a data scientist, first at Centrica, then as a founding employee of Empirisys, since January 2021.

Tell us a bit about yourself, background and your current role

I have been working in STEM-related fields since 2014 and before that graduated with a psychology degree. I have lived and worked in six different countries and speak four languages. I recently took the biggest plunge of my career by becoming a founding employee of Empirisys, a new tech startup focused on culture and safety. Empirisys is geared towards high hazard industries (oil& gas, manufacturing, constructions, chemicals, etc), but any workplace can benefit from a strong safety culture.

During my masters in social psychology at the VU Amsterdam, I developed a particular interest in human relationships and culture. I mostly learned how difficult it is to influence human behaviour, especially in such large groups as a workplace, but that is why I took up the challenge with Empirisys. What I did learn is that there are many different ways of measuring behaviour and attitudes, both directly through surveys, and indirectly, through observations. The environment or nature of the work often traps humans in committing errors they otherwise would not have made. Identifying these human error traps is the first step towards addressing them. Safety is also not just physical, it is also the psychological safety to being able to speak up and point out problems with an asset or being able to stop working when an employee perceives there is a safety risk. It’s this interplay between physical and psychological safety that fascinates me most, with one influencing the other in a continuous feedback loop.

But before joining Empirisys, I truly started my career as a data scientist 3 years ago at Centrica, where I was part of a large team of data scientists, supporting the business with business insights, process improvements, and even fraud detection. However, I soon realised that I wanted a different challenge, and more importantly I wanted to work for myself. This is how I came to work for Empirisys who fulfilled all those criteria: a small team involved at all levels of the business with a goal I could fully get behind. I learned to program during my PhD in Computer Science and Informatics at Cardiff University, which I finished as I had already started working for Centrica full-time. It is during my PhD that I learned to program for the first time, unlocking a new way of manipulating and analysing large amounts of data I had not come across before. It is in my time in industry that I learned how to effectively apply these techniques, in ways that can actually make a difference to people.

Did you ever sit down and plan your career?

My career in data science was mostly accidental. I had always thought that I would stay in academia and become a researcher. My ambition at first was to become a professor in social psychology. However, I wanted to give industry a shot, because I didn’t want to rule out something I had not tried before. As I was finishing my PhD and no longer benefited from PhD funding, I started looking for a job and was lucky to get accepted to Centrica as a data scientist. At the beginning, I thought I would do this for a year or so, and then return to academia. Working as a data scientist really suited me, I enjoyed working in a team (which was a big change after mostly working alone on my PhD!) and the structure given by agile and being part of a development team. During my time at Centrica, I got a mentor who guided me in thinking about my career and where I wanted to end up. Also talking to my peers and my managers helped me formulate bit by bit what I wanted to get out of my career.

Have you faced any career challenges along the way and how did you overcome these?

When I first joined industry, I had to adjust to the way the corporate world worked. There was no room for perfectionism in the environment I worked in. Everything I did had to be useful to the business in some way, so I had to learn to work quickly and efficiently to deliver. I set myself really high standards, not wanting to compromise the quality of my work or my time spent researching what the best technique would be. However, I quickly learned that this was not sustainable. As data scientists, we each have our specialities, whether that is a stronger background in statistics, stronger software engineering skills, or expertise in specific algorithms, such as neural networks. I learned that I did not have to be the expert in each of these domains, and that I could rely on my team members for support where needed. In return, I was able to help them in the domains they were less confident about. It’s thanks to our complementary strengths and weaknesses that we were able to address many different challenges as a data science team.

What has been your biggest career achievement to date?

One of my biggest achievements to date is getting my PhD in Computer Science, despite having come from a background in psychology and only having a very rudimentary understanding of programming before becoming a PhD candidate.

More recently, I was part of the 4-member founding team of Empirisys. I would have never thought that I would be part of setting up my own business, and to be able to do this so soon after launching my career in data science feels like a great achievement.

What one thing do you believe has been a major factor in you achieving success?

I am adaptable, which has helped me switch career paths from psychology to computer science, despite having no idea about computers or knowing any programming languages. I never saw obstacles, but rather new things I needed to learn in order to achieve my goals. It has also helped me feel comfortable moving around for my studies, which has been a very valuable experience. When it came time to look for a PhD, it was very easy to make the decision to come to Cardiff, as I had no doubt that I would be able to adapt to a new country rapidly. As a data scientist, it has helped me throughout my career as I learned to work in a larger team, after having worked mostly on my own during my PhD.

What top tips would you give to an individual who is trying to excel in their career in technology?

I think your ability to learn and your ability to adapt are the two skills that are most important in a career in technology. The field is moving so fast, that it is essential that you learn to quickly switch gears when something is not working, and that you stay on top of the latest trends. It is also important here to be able to distinguish between what is a genuinely useful new technique or programming language, and what is just a fad. This will come with experience, but also talk to your peers. Join meetups or networks or have regular get togethers with the other tech members of your company to discuss new things you learnt and share this with your colleagues.

Do you believe there are still barriers for success for women working in tech, if so, how can these barriers be overcome?

I have been lucky enough to not have experienced direct barriers so far in my career. I have noticed a lack of women in leading positions, so I wonder how I will feel about this question once my career progresses. I was lucky to have a very driven female mentor, who helped me be aggressive about my career. This has helped me be more pro-active about what I want, but also understanding what I value and want to get out of my career.

I think things are moving in the right direction, but there are still unique challenges such as maternity interrupting women’s careers and unconscious bias that might be barriers to hiring and promotion.

What do you think companies can do to support to progress the careers of women working in technology?

I think that the culture of the company is important in making sure that everyone in the company is listened to and taken seriously by all levels of the organisation. An open and inclusive culture can help with this. However, it’s a concerted effort to change the workplace’s culture from all people involved. I think especially peers are important to set examples and rectify unwanted behaviours, such as discrimination or lack of respect for employees. I also think there is a responsibility for recruiters to consider whether the values and soft skills of the people they are employing match the culture they want to develop at the company. A sense of responsibility from everyone to make the workplace a pleasant and productive environment, where diversity of experiences is valued.

There is currently only 17 per cent of women working in tech, if you could wave a magic wand, what is the one thing you would do to accelerate the pace of change for women in the industry?

Having recently been involved in recruiting for our new startup, I noticed that we had much less women apply to our positions than men. This already narrows the pool down. So there definitely is some work to be done on the pipeline of talent coming through. One important aspect is to demystify a career in technology and make it seen as accessible to everyone. I think there might still be a bias where some people think: “Oh no I would never be able to do that”. In high school, maths was one of the subjects I struggled with most and I felt like it was just not the subject for me. However, thanks to my very supportive family, I was able to overcome this mentality, and gradually improved in the subject and passed my final year exams very comfortably. I will never be a maths genius, but I have learned that I can achieve things by working hard and staying dedicated. It’s this mentality that I want to foster in students in high school or university who are thinking about their future careers.

In the same vein, I want people to consider a switch to a career in tech as an exciting challenge. I was part of a panel for university students where 5 speakers, all women, explained how they had turned their careers and gotten into tech without having gone through the more traditional pathways. I think the panel was a great inspiration for everyone present that a career switch is not only possible, but also often an enjoyable experience where we get to discover a whole new set of skills, but also apply all the things we have learned from our previous roles.

At the end of the day, I just want everyone to be encouraged in going down the path they want to, whether that is a career in tech or not. I don’t want to be a women in tech, I want to be in tech.


WeAreTechWomen has a back catalogue of thousands of Inspirational Woman interviews, including Professor Sue Black OBE, Debbie Forster MBE, Jacqueline de Rojas CBE, Dr Anne-Marie Imafidon MBE and many more. You can read about all our amazing women here


Inspirational Woman: Maria Apostolopoulou | Data scientist, bp

Maria Apostolopoulou

I have always had a passion for sciences – maths and chemistry in particular – and this pushed me to pursue my bachelor’s and master’s degrees in chemical engineering in Greece, where I am originally from.

After this, I moved to London for my PhD in chemical engineering at UCL, where my research was focused on developing and improving stochastic simulations – a type of simulation where variables change randomly with individual probabilities – to better understand how fluids are transported underground.

After many years in academia, I felt like it was time for a change. I wanted to be part of a team and feel the buzz of people around me, but I never imagined myself as an engineer working on-site with a hardhat. I also knew that I wanted to contribute towards helping the environment – climate change is something I think about a lot. So, I applied to bp’s Challenger programme.

I joined as a software platform engineer initially, and as part of my second rotation I am now a data scientist. At the time, bp was one of the first energy companies moving its data to the cloud. To me, this was a strong sign that it was truly committed to using technology to have an impact, and that it was committed to acting now.

My team works on different projects that come to us from all across bp, and never before have I felt such a strong sense of teamwork. We are currently working on a project to aid the decision-making process for our operations. By looking into historic data collected from sensors in our sites, we try to predict when equipment maintenance will be required. Knowing when our systems will stop operating at optimal conditions can help us schedule our maintenance activities without impacting our sites' efficiency or compromising operational safety. This kind of optimisation also reduces costs, which in turn allows for greater investment elsewhere, including our low and no carbon energy businesses. To me, this is the perfect example of how, in their own way, everyone at bp has a role to play in creating a net zero future.

Outside of work, I love arts and crafts! One of my recent works is a water fountain which I designed, assembled and decorated from start to end. I also really value mindfulness and doing things which bring me close to nature. With COVID-19 restrictions, I regularly go running or cycling.

Did you ever sit down and plan your career?

Yes – it’s something I do quite frequently actually! I like to have a general idea of where I would like to go. For the short-term I like making plans, setting goals, and having aspirations as it drives me to continuously improve towards my long-term goals. As with everything in life, sometimes I get it right, sometimes I need to take a step back and re-evaluate before trying something different. As I grow older and gain more experience, I find it easier to adapt and manage my expectations, but it hasn't always been easy. Having mentors and people I look up to has also been a very valuable resource and has helped me recognise that everyone’s career pathway is different and often non-linear.

Have you faced any career challenges along the way and how did you overcome these? 

During my PhD, I studied something very specific. When it came to my area of research, I knew that I was truly on top of my game, and I had my own ways of working which worked well for me. Moving away from academia and joining the Challenger programme at bp, I suddenly no longer felt this way. I had to learn everything about this new environment and figure out how the teams operated. I invested time in learning new skills and familiarising myself with the business environment and terminology, and the structure of the Challenger programme gave me the time and tools I needed to overcome the hurdles I was facing. I also attended development courses that helped me adapt to the new ways of working. This career challenge helped me grow both personally and professionally, and I was able to make new friends along the way.

What has been your biggest career achievement to date?

I am very proud of the trust I rapidly gained from my colleagues. From day one, I got to work on actual problems with real implications for my teams and for bp. In particular, I was involved with the migration of our repositories and pipelines to Microsoft’s Azure DevOps platform, which helped the team improve its efficiency and increased the reliability of the services we provided. Having a close team meant I could learn from them quickly to deliver what was needed – I was therefore given a lot of responsibility, which felt great! As part of this, I am proud of how I handled challenges – big or small – through clear communication and proactive thinking.

What one thing do you believe has been a major factor in you achieving success?   

My mother has been my biggest role-model. As an entrepreneur and natural problem solver, she showed me that through hard work and consistency, you can achieve anything you aim for. This mindset has helped me overcome difficulties along the way and pursue a career in an industry that has historically been dominated by men.

What top tips would you give to an individual who is trying to excel in their career in technology?

Don’t let fear or doubt stop you! Even if there’s something that scares you – just say yes. Without this attitude, I would have missed out on so many unique opportunities and I wouldn’t be the data scientist that I am today. The best way to learn something is by doing it.  We live in an era where information is easily accessible and often free and there are so many great open source tools to help you get started. It’s also important to follow tech-related discussions on social platforms as you can get a good idea of the emerging trends.

Do you believe there are still barriers for success for women working in tech, if so, how can these barriers be overcome? 

Although improving, women are still underrepresented in tech. In my opinion, the lack of flexibility in working arrangements contributes to this. A significant proportion of jobs in tech can be done remotely, so maybe it’s time to challenge the traditional ways of working. And one thing that the recent pandemic has taught us is that we have the framework and tools to support flexible working, while maintaining productivity.

What do you think companies can do to support and progress the careers of women working in technology?

I think having a structured career development pathway that engages all employees would help address some of the gender inequality, especially in more senior positions. Proportional representation is important as role models can influence career aspirations. Companies should also have dedicated senior leads that act as advocates for the progression of women and other underrepresented groups within the company.

There is currently only 17 per cent of women working in tech, if you could wave a magic wand, what is the one thing you would do to accelerate the pace of change for women in the industry?  

It’s fundamental for young women working in technology to have female role models they can look up to. When I went in for my technical interview at bp, I was really surprised to see a female interviewer. It was so inspiring and energising to see a woman in a leading tech position; unfortunately, it was something I had never come across before. It made me feel like bp was a company that would be fully supportive of me exploring my passions and ambitions. There is still a lot that needs to be done to increase the representation of women in tech – and having female role models is an important starting point.

What resources do you recommend for women working in tech?

Wired and Towards Data Science are my go-to resources for anything related to digital developments. They’re great ways to be on top of current and future developments in the tech and data science world, and to exchange ideas on these topics. For those who prefer podcasts, I highly recommend the Data Skeptic, which is one of the longest running and a personal favourite. Finally, to get hands-on experience, Kaggle has tutorials, free datasets, numerous competitions, and a vibrant community of data scientists – all the ingredients you need to get your career started!


WeAreTechWomen has a back catalogue of thousands of Inspirational Woman interviews, including Professor Sue Black OBE, Debbie Forster MBE, Jacqueline de Rojas CBE, Dr Anne-Marie Imafidon MBE and many more. You can read about all our amazing women here


Inspirational Woman: Alicia Frame | Lead Product Manager & Data Scientist, Neo4j

Alicia FrameMy name is Alicia Frame, and I am Neo4j's Lead Product Manager and Data Scientist.

I work in our Product Management team to set the roadmap and strategy for developing graph-based Machine Learning tools.

I have a Doctorate in Computational Biology from the University of North Carolina at Chapel Hill, and a first degree in Biology and Mathematics from the College of William and Mary in Virginia. I have over eight years of experience in enterprise Data Science at places like Benevolent AI, Dow AgroSciences, the US EPA, and now Neo4j.

I think Data Science can seem a bit like alchemy, or a Dark Art… people get advanced degrees in interesting but esoteric stuff, they use complicated and obtuse methods to transform your data into black box predictions, and you’re supposed to trust the results. I see my mission as to democratise AI — taking rigorous science and making it possible for anyone who downloads the software to run highly predictive, powerful algorithms. We’re taking all these incredibly academic science methods and making it possible for anyone who downloads our software to run algorithms with a few commands. We want to get it to the place where a user doesn’t need to have completed a dissertation in Network Theory or Deep Learning to have access to highly predictive, powerful methods; she just needs to know the question the business wants to answer, and the data to use.

I am super-excited about some of the things we have planned for our next release of the Neo4j graph database to help do that. My team is working on prototyping several different implementations of graph ‘embeddings’, and we’ll be the first software available that offers them on an enterprise scale. Embedding is a way of representing data in lower dimensional space (think of a short summary of a complicated concept) that is machine-readable. It’s a way of taking all the information about nodes in a graph (or a word in a document) and summarising the important parts, and you can use that summary to predict outcomes, properties, or classes.

It’s a very powerful machine learning technique, but normally you usually need to code something from scratch, and you can really struggle with big data sizes. We’re democratising the technique so anyone can calculate an embedding for their graph data with a few simple commands, which is, I think, a great innovation.

Did you ever sit down and plan your career? 

Not really! If you had asked me when I was in college if I expected to be a Data Scientist for a graph database company, I would have had no idea what that meant, and laughed at you. I was always good at maths and computer science, but I had no confidence in myself, and thought of them as dorky hobbies. I originally wanted to be a biologist, but I was a miserable bench scientist and terrible in the field… I never had the patience or precision for that kind of slow, meticulous work.

What I was good at, though, was writing code to analyse my data and building mathematical models to describe the patterns I expected to find. While I was getting my degrees, I’d managed to pick up concentrations in Maths and Computer Science based on coursework I was taking to satisfy my curiosity. About halfway through my Doctorate, I realised I could do the things I loved full time as a career.

My first job in graduate school was as a database administrator for a Federal database of toxicological data. I didn’t know SQL until I qualified for the phone interview and found out I would be evaluated on my skills with it. The night before, I went through an entire ‘Teach Yourself SQL in 24 Hours’ book and somehow got the job, I’m relieved to say.

Ever since that first opportunity, data, and databases, have defined my career.

Have you faced any career challenges along the way and how did you overcome these?

I struggled for years with imposter syndrome. I was afraid someone would suddenly realise I wasn’t really qualified or wasn’t as smart as I was pretending to be. What I know now: If something isn’t working for you in your job — you’re not challenged or respected, there’s no growth trajectory for your role, your workload is insane, whatever it is — you need to speak up. You’ve got highly in-demand skills, and there are plenty of places looking to hire, so don’t waste your time if a company or position isn’t the right fit for what you want to pursue.

What has been your biggest career achievement to date?

I’ve had a lot of different jobs, and I don’t think any-one’s role is a bigger achievement than another. What motivates me is building tools and programs for people to address their own problems: for example, building tools so people can use graphs to cure cancer, or writing policy to keep children from eating lead paint, or building models to predict and screen out pesticides that can cause cancer, it’s about doing good, regardless of your situation.

To me, it’s the driving force behind why I love building tools is to enable others to do amazing things. I’m good at it, and that allows others to shine. It’s also about more personal matters, such as taking the time to be kind or mentor someone, or just listen and make them feel valued. To me, these are just as or more important as getting a flashy award or big promotion.

What one thing do you believe has been a major factor in you achieving success?  

My biggest break came from mentoring from a super strong all-female team. I had spent most of my career working with men and learning how to mask my opinions. I’d internalised fear of being seen as too abrasive or direct, but that changed that. After I was in the office for a week or two, no one told me to be less me. I remember one of these women mentors coming to my desk and telling me to just spit it out in meetings and say what I meant, instead of hiding behind lots of platitudes.

They never told me to be quiet or keep my opinions to myself. They made it clear that they knew I was smart, and they trusted me to make decisions.

What top tips would you give to an individual who is trying to excel in their career in technology? 

Cut through the noise, and only ever focus on what’s really important. Ignore MOOCs, Kaggle competitions, and the social media Data Science influencer echo chamber. No-one was hired on the basis of online credentials or the number of retweets on a post. Also, look for opportunities to take on Data Science projects and tasks in any role

Don’t be afraid to make moves or shift your career in a horizontal direction. If something isn’t working, speak up. You’ve got in-demand skills, and there are plenty of places looking to hire, so don’t waste your time if a company or position isn’t the right fit.

Always come prepared. Make sure to build a portfolio of projects to showcase your experience. Be able to explain the question or problem at hand, how you solved it (both in plain language and technically), what the business value was, and what your specific contributions were.

Sharpen your soft skills — they will take you farther than you think. Given the nature of the software industry, many individuals forget that it’s important to work on and value capacities for active listening and compassion. Plus, being great at networking and building relationships with others is a key element to building a successful career path.

Do you believe there are still barriers for success for women working in tech, if so, how can these barriers be overcome?

There are certainly barriers for women to overcome: I’m cognizant of the fact that while I am a successful female data scientist, I’m often the only woman in the room. I am always aware of how what I say will be perceived; I know that, as a woman, when I express disagreement, it’s more likely that I’ll be perceived as ‘bitchy’ or angry, instead of raising a valid point, or providing constructive criticism.

I think it’s important to take women at face value. Don’t apply gendered assumptions to what I say. I don’t have to be the nurturer on the team and I can disagree with you without it being personal. Unaddressed expectations about emotional labour, or how women should present themselves, are one of the most difficult things to overcome.

What do you think companies can do to support and progress the careers of women working in technology?

  • Don’t have strong/rigid requirements on years of experience or specific skill sets. Women are less likely to exaggerate their skills or experience than men, and I think this can lead to them being screened out
  • Invest in women: we’re less likely to speak up and demand resources like external coursework or coaching, but it doesn’t mean we won’t benefit.

There are currently only 17% of women working in tech, if you could wave a magic wand, what is the one thing you would do to accelerate the pace of change for women in the industry? 

Get girls involved in maths and computer science early. Part of the problem with women in tech is that there aren’t enough of us. Women aren’t majoring in science, technology, engineering, and mathematics (STEM) courses, which leaves us underrepresented professionally. It wasn’t always this way; originally, programming was seen as a woman’s job, but today, girls get discouraged from technical fields early on (it’s too geeky, they don’t have the background) and it’s an uphill battle from there.

If I could, I would make every secondary school girl take a course on the basics of computer programming, in a supportive environment with female mentors and instructors. I think this would lead to massive changes in the industry. Change only happens when you demand it, and we need more female voices in the industry calling for equity and representation — and education is where we start.

What resources do you recommend for women working in tech? 

It’s worthwhile to have a social media presence on LinkedIn, Twitter, Medium, etc - but don’t let that distract you from building a solid CV. I’m a big fan of reading original peer-reviewed publications and finding code from the authors, to learn more about new techniques and the state of the science. For conferences, I usually follow the ‘gold standard’ machine learning conferences (NeurIPs and ICML), but I also would recommend following something specific to your field of interest (I’m a big fan of the American Chemical Society and the Royal Society of Chemistry).


WeAreTechWomen has a back catalogue of thousands of Inspirational Woman interviews, including Professor Sue Black OBE, Debbie Forster MBE, Jacqueline de Rojas CBE, Dr Anne-Marie Imafidon MBE and many more. You can read about all our amazing women here


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Women in tech - the why, what and how of building a career in data science

binary code, data scientist

By Joanna Hu, Principal Data Scientist, Exabeam

With a growing number of organisations recognising the financial, social and cultural benefits of recruiting more women into data science, isn’t it time to explore the opportunities on offer?

Like many women who graduate with a tech degree, it took me a couple of years to figure out that data science was my niche. Thankfully, I eventually found my way and went on to forge a rewarding career in this exciting field.

With advancements like machine learning and big data now in the frame, I’ve been lucky enough to contribute to discoveries and solve real-world problems in healthcare, energy, and now – as principal data scientist at Exabeam – the cybersecurity industry.

I’m not alone in thinking that data science is a rewarding field to work in. Based on overall job satisfaction scores, the role of data scientist is ranked #7 in the Glassdoor ’25 best jobs in the UK for 2019’ listing – with an average base salary of £46K.

A long heritage

Historically, women have made a significant contribution to the evolution of computer science.  Before the invention of electronic computers, women were more prominent in the computer science field, and contributed a lot to the invention of the first electronic computers.  As well as Joan Clarke, who worked alongside Alan Turing to crack the Enigma cyphers during WW2, the other female codebreakers at Bletchley included Margaret Rock, Mavis Lever and Ruth Briggs.

More recently, there’s been trailblazers like Dame Steve Shirley, who first embarked on a technical career at the prestigious Post Office Research Station in Dollis Hill, where the Colossus codebreaking computers used at Bletchley were created. Founding her own software company in 1962, her team of female freelancers would go on to undertake many cutting-edge projects – including programming the black box flight computer used in Concorde.

Today, a new generation of women are forging their futures within the tech sector. Coming from a diversity of backgrounds, they’re making great strides in the field of data science – and many have done so without an initial background in science, technology, engineering or mathematics (STEM).

A field rich with opportunities

Make no mistake, data scientists are in high demand. A recent study found that 80 per cent of UK businesses are looking to hire a data scientist in 2019, and IBM estimates that by 2020 the demand for data scientists and analysts will leap by 28 per cent.

That said, while women represent 47 per cent of the UK workforce, they only hold around 19 percent of all available tech jobs. Clearly, it’s time to redress the balance.

That’s certainly the opinion of bodies like the Alan Turing Institute and organisations like the International Women’s Day (IWD) movement. Indeed, the IWD #BalanceforBetter 2019 campaign is making great strides in changing hearts and minds – by showcasing how women in tech are achieving impressive outcomes for themselves and others.

The good news is a growing number of companies now acknowledge there are significant gains to be won by addressing the issue of gender inequality in their tech workforces. As a result, they’re eager to hire more female data scientists. Indeed, Gartner projects that in the next three years, both women and men will equally populate the role of chief data officer (CDO).

Why companies want more women in data jobs

Research organisations like McKinsey have found that highly diverse companies are 15 percent more likely to outperform those that are not gender diverse. Alongside enhanced financial performance, reports by analysts such as Morgan Stanley, McKinsey and Gartner confirm that having more women in the tech workforce creates a more cooperative and collaborative atmosphere.

Their research findings also highlight how women are more aware of risk, which in the field of big data is a major plus. What’s more, women tend to excel at communication, team nurturing and problem-solving—all vital qualities when working in the field of data, where outcomes depend on asking the right questions, and listening to the answers.

Finally, and perhaps most interestingly, the research findings illustrate how women are strong advocates for data-driven decisions and tend to be more solution-oriented than male counterparts.

I’m not a rocket scientist – can I make it in data science?

Absolutely. If you’re a curious person, are passionate about innovation, and have an interest in technology, then this may well be the career for you. Stephanie Glen’s recent blog – charting her life-changing journey from office cleaner to data scientist – highlights that as far as she’s concerned, a love of logic problems is the most important pre-requisite for the job.

Typically, the skill sets required include math, statistics, coding and system design. But, as a recent article in CIO magazine highlights, exacting true business value from data requires a unique combination of skills that includes storytelling and intuition.

Truth is, women with a passion for learning who want to try something new will find there’s a number of big-name tech companies out there that only too ready to help you develop the digital skills you need to embark on a career in data science. Plus, there are organisations like Girl Geeks that are proactively supporting women to enter and progress in the field.

Top tips?

If you’re already working in the tech field, or are ‘data science’ curious, then teach yourself the data science knowledge and network as much as you can.  Before deciding this was the path I wanted to commit to, I spent time talking to people about their work, went on workshops, joined weekend meetups and tried out small projects from the online courses.

These days, there are lots of resources available to women who want to make a go at it in this field. Find out about which new tools you’ll need to learn, then use your free time to hone your skills – pretty soon, you’ll become an expert.

When it comes to seeking out new job opportunities, follow good companies and people rather than high salaries. Ideally, you’ll want to work for companies that have intelligent leaders and care about their female talent. Most importantly, hunt down a great mentor and commit to continuously learning from superiors and peers.

Finally, believe in yourself and, no matter what roadblocks you face on the journey, don’t let anyone limit your potential.

Joanna HuAbout the author

Joanna has rich industrial working experience within data mining and big data analysis for healthcare institutions, energy companies, and retailers. Through her work she aims to help them identify frauds, predict risk and outcome, reduce cost, and estimate product qualities.

Joanna has a Ph.D. from University of California, Berkeley, in Nanotechnology and a Ph.D. from University of Michigan in computational earth sciences. Before joining Exabeam in 2015 as a senior data scientist she worked at Ayasdi as a data scientist building and improving algorithms for client healthcare institutions to produce the best treatments for patients. Since October 2018 Joanna has been principal data scientist at Exabeam.


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Inspiring Women in STEM this International Women's Day

Article by Noha Badawy, Data Scientist at Dataiku

women in tech, soft skillsInternational Women’s Day is a time to celebrate the fantastic accomplishments of women around the world.

Some of these female role models may have what seem like unobtainable achievements or almost unimaginable skills and talent, however, all of these women started off just like you and me.

As a career path and as an industry, data science and its associated opportunities are growing at such an unprecedented rate, that it can often be difficult to keep on top of everything that is happening, as well as every new technology popping up. However, as quickly as the sector is progressing, there’s a divide in that women data scientists are not entering and moving up through the field at the same pace as men.

Encouraging more women to apply for data science roles and transition to data science as a career is something I am very passionate about. It may seem an intimidating field, but what’s amazing is the number of people who came to data science from other fields: after all, we’re all consuming and interacting with data science at a much higher level than we probably realise.  I hope my own journey into data science can inspire other women to consider whether it’s a career for them, and that they receive the same motivation and encouragement I have received from my team along the way.

The Wakeup Call I Needed to Pursue a Career in STEM

As I started my studies, I thrived on studying anything that was put in front of me, however, when it came to choosing a degree and a career path, I initially put my interest in computer science aside and chose a business and accounting role. However, while working in project coordination for senior executives, which involved following up on some data projects actions and progress, I asked one of the team members for advice and they responded with information I didn’t understand. This was the wakeup call I needed to pursue a career in STEM.

It was in this moment that I realised that there was nothing stopping me from retraining as a data scientist and becoming the person who could understand what my colleague was trying to explain. Computer science had always been my original passion, and I decided that now was the time to embark on the master’s degree that would give me the knowledge and the skillset I needed to pursue that passion.

I already had basic knowledge of statistics and maths, but the evening classes for my master’s degree went much deeper. I grew up in Egypt, so the maths and statistics I covered in school were in Arabic. It initially felt like starting over and relearning from the beginning, but eventually it clicked. I graduated with a master’s degree in Business Intelligence and Analytics, which covered statistics, mathematics, data mining, machine learning, business and risk modelling.

It was from here that I shifted my concentration to more of a data science focus and began teaching myself Python, before beginning a full-time data science role. Now in this role, I focus on training and coaching. This not only includes demonstrating to others how to use the platform, but also teaching the art of data science. For me, this includes simplifying the concepts that I need to explain and finding relevant examples to help people understand. I also motivate participants to have the belief that data science isn’t out of their reach.

My own learning did not stop there, though. With the advancement of new technology, it’s important to keep up with the rapidly changing landscape. However, diversity in the data science industry is not necessarily advancing at the same pace. Most applicants for data science roles are men, which shows that many women are reluctant to take risks and apply for roles that they do not feel 100% ready for. More women need to be encouraged to step out of their comfort zones and to pursue their passion or a career that they did not initially consider to be for them.

One way to encourage more women into STEM roles is to cut out the negativity and noise that questions whether women can code, or if something is too technical. Many people are positive and helpful when it comes to women in data science, yet a large portion can still fuel the negativity and stereotypes around STEM being a male-dominated industry.

Because of this, it’s important to be relatable. For those looking to enter a data science – or similar – role, it is helpful to recognise that even the most experienced STEM professionals don’t fully understand new concepts first time around. Sometimes it takes watching several tutorials before the information is digestible.

Attending events and joining discussions can help to break down those barriers and to show women that there are many role models out there. My key piece of advice for a woman looking to enter the STEM sector is for them to recognise that they are just like everyone else with a passion to learn and develop their skills, so it is best to follow your intuition and ignore the sometimes negative and dismissive opinions of others.

About the author

Noha started her career at Barclays bank in Cairo as a Customer Banking Business Support Manager before progressing into a Data Visualisation and Data Science Role. At Dataiku, Noha is an accomplished Data Scientist with training in Machine Learning, Operational Research, Big Data, Business Simulation, Data Mining and Python.

In addition to her own achievements and challenges encountered in her career to date, Noha is passionate about giving advice to companies on building inclusive environments and how to best address the main difficulties encountered by women in tech today.


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Inspirational Woman: Nicole Angell | Junior Data Scientist, Carbon

Nicole AngellNicole Angell has recently joined Middlesbrough-based Carbon data management platform and hope their stories can encourage other women to consider a career in technology.

Carbon uses Artificial Intelligence and machine learning to better understand online customer behaviour in order to help its customers personalise content and advertising for their audiences.

Every day, Carbon collects and analyses anonymous data from more than two million new unique users to understand customer behaviour and intent.

Nicole Angell, 23, is a junior data scientist who has been with Carbon for six months after completing a degree course.

Tell us a bit about yourself, background and your current role

I’m 23 years old and I graduated with a first class degree from the University of Stirling last year. The course title was BSc (Hons) Mathematics and its Applications. I have been working at Carbon as a junior data scientist for the last seven months and joined the company straight after finishing my degree.

Did you ever sit down and plan your career?

I did sort of plan my career. When I started my degree, I started looking into careers and decided data science was perfect as it combined my interests for maths and coding.

Then I chose modules and projects at university that allowed me to work towards my desired career.

Have you faced any career challenges along the way and how did you overcome these?

I’ve only recently joined Carbon, so no career challenges so far.

What has been your biggest career achievement to date?

My biggest career achievement to date has been being able to contribute to developing new features and tools for the company and getting my work into production.

The maths side of my work relates to data analysis to help clients understand their audience while the coding aspect helps me develop features to help enhance who they advertise to.

Our work is valuable as we help make companies become more profitable and identify the right audience enabling them to get to the right people.

What one thing do you believe has been a major factor in you achieving success? 

A major factor in achieving success was putting the effort in. There was clearly a lot of maths on my course, but there was only one module on coding so I did short courses in my spare time to learn about this area of work.

This meant I was ready to go into the industry with the skills I needed to be a data scientist.

What top tips would you give to an individual who is trying to excel in their career in technology?

I would suggest keep pushing yourself to learn new things. In this type of job there’s always more to learn.

Do you believe there are still barriers for success for women working in tech, if so, how can these barriers be overcome?

No, if you work hard enough I don’t see why there would be barriers.

What do you think companies can do to support and progress the careers of women working in technology?

I think it’s more down to the individual, but at Carbon, I’m really enjoying it – everyone works together and is supportive – and the job gives me a good balance between maths and coding.

More women are coming into this industry than ten years ago, but not as many as there could be so I would encourage others to think about this sort of career – especially if they like maths. The industry is getting bigger with more and more jobs being created so it is a good career you can progress and continue to learn in.

There is currently only 17% of women working in tech, if you could wave a magic wand, what is the one thing you would do to accelerate the pace of change for women in the industry?

I think more emphasis needs to be put on introducing things like coding earlier in education and encouraging learning in that area.

After having chats with other females on my university course, I realised none of them wanted to choose that module even though they’d never tried coding. I think that’s down to the fact its ‘new’ to them and if higher level computing was compulsory at school/college the module would’ve been more popular.

I would introduce lots of conferences available across the UK for women in tech/data science/computer science careers.


Inspirational Woman: Ela Oftadeh | Data Scientist, Carbon

Ela OftadehEla Oftadeh has recently joined Middlesbrough-based Carbon data management platform and hope their stories can encourage other women to consider a career in technology.

Carbon uses Artificial Intelligence and machine learning to better understand online customer behaviour in order to help its customers personalise content and advertising for their audiences.

Every day, Carbon collects and analyses anonymous data from more than two million new unique users to understand customer behaviour and intent.

Ela Oftadeh is a KTP associate on a data science project collaboration between Carbon and Durham University. The Knowledge Transfer Partnership matches up businesses who want to innovate, develop and grow, with a university which has the expertise to help them.

Tell us a bit about yourself, background and your current role

I am aged 37 and have a PhD in Statistics from the University of Kent. I started my first job as a pricing analyst in 2018 with an insurance company.  I joined Carbon in December 2019. I am working as a data scientist (KTP Associate) for the company and also with an academic team at Durham University. Using machine learning methods such as classification and clustering, and Bayesian statistics where probability expresses a degree of belief in an event, my work using statistical modelling helps me to identify the right audiences from data analysis.

Did you ever sit down and plan your career?

During the final year of my PhD, I started thinking about my future career. I didn’t have a detailed plan for my career, but I had a clear goal which was becoming a data scientist.

Have you faced any career challenges along the way and how did you overcome these?

For someone who had just graduated, not having any experience in industry was a big issue. Most of the jobs were suitable for someone with some experience. I had to spend some time to do some self study and learn some of the skills.

What has been your biggest career achievement to date?

Since I am in the very beginning of my career path and I am still learning, so I think it’s too soon to answer this question. I think at this stage having a job that you enjoy is the best achievement. Carbon is a great environment to work in and I am really enjoying being surrounded by very supportive and skilled people. The job itself is also a great opportunity to potentially put what I have learnt so far into practice.

What one thing do you believe has been a major factor in you achieving success? 

I’ve always tried to keep going and not give up when it gets hard. A KTP project is a good career start for someone who has graduated as it fills the gap between industry and university in terms of exploring a theoretical field within industry.

Longer term, I would like to improve my skills in data science as much as I can to be able to explore more complicated areas and hopefully secure more senior positions.

What top tips would you give to an individual who is trying to excel in their career in technology?

As already outlined, I am still in my early stages of my career, but as a learner, I think staying up to date and keep learning to keep up with the technology advancement.

Do you believe there are still barriers for success for women working in tech, if so, how can these barriers be overcome?

There are fewer women than men in this industry so hopefully my story can inspire other women to join. I can’t really see any barrier for women. There are a lot of opportunities out there for everybody and I think the main thing is getting the right skills and go for it.

What do you think companies can do to support and progress the careers of women working in technology?

I think for those who have families, it would help a lot if there is an opportunity to work remotely. Having flexible working hours will also be helpful.

There is currently only 17% of women working in tech, if you could wave a magic wand, what is the one thing you would do to accelerate the pace of change for women in the industry?

Having career events at universities would be a great way of introducing the job market. Students may not have a clear idea about their future career but attending career events and talking to people in different areas may give them a new perspective about opportunities.

What resources do you recommend for women working in tech?

It really depends on the area that they work. I actually started this job a month ago and I don’t have enough information yet. I think in data science, online courses and using useful websites like data science central could be useful.


Inspirational Woman: Dr. Kiki Leutner | Business Psychologist & Data Scientist, UCL

Kiki LeutnerDr. Kiki Leutner is a business psychologist and Data Scientist at University College London (UCL).

She is Director of Assessments and Innovation at HireVue, where she develops innovative, data driven assessments that are fair and psychometrically valid. Her academic work is published in peer reviewed journals, including work on the intersection of machine learning and psychometrics. She is an expert in innovative psychometric assessment, personality theory, and behavioral analytics.

Tell us a bit about yourself, background and your current role.

I currently work as Director of Assessments Innovation at HireVue and also as a lecturer in the psychology faculty at UCL. HireVue provides video interviewing and talent assessment solutions used by over 700 organisations globally to transform the way companies discover, hire and develop the best talent. My role at HireVue is to ensure that we build the fairest and most predictive pre-hire assessments possible, using the wealth of technology and science available to us. I believe that the key is bringing together business psychology and data science and machine learning.

I came to the UK for university, studying a combination of Philosophy, Psychology and Computer Science. I undertook a PhD at UCL, sponsored by the Engineering and Physical Sciences Research Council (EPSRC), which allowed me to learn about machine learning and data science, and bring this to my Psychology research. I started focusing on developing new methods of personality profiling. For example, I used free text data to develop personality profiles, and also developed an image-based personality test.

There’s so much discussion around ethics in Computer Science. It’s important to appreciate the context of human behavioural data and the specific implications it has. There is a longstanding tradition in Psychology to carefully evaluate datasets. And specifically, in Business Psychology, to check and evaluate how algorithms affect different groups of people, and to make sure they are fair. By working at the intersection of data science and psychology, I try to bring the two together. It is also the focal point of a class I teach at UCL. I lecture both Computer Science and Psychology students, bridging the gap between methodology and specific concerns in handling human behavioural data, whilst bringing a psychology ethics perspective to both.

Did you ever sit down and plan your career?

I never planned my career, but I am always thinking about what I will do next. I am the kind of person who can never do just one thing at a time – I always have several projects on the go.

I feel as though I have been very fortunate in the opportunities that I have come across, and the mentors I’ve met along the way. I try to only pick opportunities that are truly of interest to me, and where I feel good about the people I’m working with. For example, I started working for MindX (later acquired by HireVue) when it was a young start-up because I was very impressed with the fast progress that they were making, and because my work was central to their mission and product.

Have you faced any career challenges along the way and how did you overcome these?

There are challenges in every career, as well as in life in general – it’s important to find a situation in which you are comfortable, working with people who care and are passionate.

In terms of how I overcame these challenges, I strongly believe the answer lies in the people you work with. Having a good team really accelerates your output and shows the value of working with a diverse group of people – everyone brings something different to the table.

Working in technology and academia, a constant challenge will always be the lack of gender parity – you are almost always the only woman, or one of few women. This has meant throughout my career, I’ve had to ensure I’m strategic in how I navigate certain situations. I always wanted to stay true to myself and speak up if I felt something wasn’t right. I believe that being true to my values has worked in my favour.

What has been your biggest career achievement to date?

Learning to trust my own judgement and ability! Especially as a young woman, you probably have more of a clue than people might make you feel. Most people don’t know what they’re doing either!  It’s so easy to become preoccupied by how other people may see you, so empowering myself to trust my own judgement is really important. It’s uncomfortable but it’s totally worth to keep insisting and making sure that people are aware of your background, title, or the work that you do, and to push your own agenda. Do the hard work, but don’t forget to claim your reward for it!

What one thing do you believe has been a major factor in you achieving success?

Balance! It’s key to have good friends, family and partners in your life, to create a really strong support network. You need to set up a good life for yourself, otherwise you can easily burn out.

I think that this is particularly relevant in the start-up world. You always have to give your most and there are high stakes and high emotions. Having stable relationships and supportive people help to balance this out.

Another key factor is having great mentors – for women especially. Without mentors, I wouldn’t have been able to negotiate things like salary and I probably would’ve said yes to opportunities that weren’t right for me! Knowing that you have someone to turn to when it comes to big decisions helps to build your confidence.

What top tips would you give to an individual who is trying to excel in their career in technology?

Surround yourself with good, supportive people and find mentors you trust and that inspire you. Education, whether formal or not, is so important – I never stop learning and would advise anyone trying to excel in their career to do the same. Trust your instincts with which jobs are right for you and don’t compromise.

One of my mentors always says, “do the job that you want to do – don’t wait for someone to give you permission, just do it.”

Do you believe there are still barriers for success for women working in tech, if so, how can these barriers be overcome?

Of course, there are barriers – not just for women in tech, but for all women. Until women can benefit from the same support from the law, the government, the people around them, they will always be at a disadvantage. Technology is a very competitive industry, so I suppose that often results in people trying to drive out women more – it’s high stakes, both in terms of money and prestige.

I try to lead by example and show that it can be done – it’s important to individually empower women in tech, rather than only speaking about the topic as a whole. One of the best ways to overcome these barriers is to find other women in tech and talk to them! It’s really important to have open conversations – things that we experience in the industry are being experienced by many other women. Shared experiences are valuable and give credence to how you are feeling.

What do you think companies can do to progress the careers of women working in technology?

I think having structure within the company is key – formal pay structures, for example, have shown to reduce the gender pay gap. Another really important aspect is parental leave – I believe there should be parental leave for both men and women that is normalised and won’t disadvantage any particular individual.

Businesses should see increasing diversity as a great opportunity, as it truly is beneficial – it has been shown by studies time and time again that a diverse workforce makes for a more productive and profitable business. Most of all, businesses need to empower and support the minorities in their company – give them true opportunity and create and inclusive culture that values competence.

I find it really encouraging to work for a company that doesn’t just talk the talk, but also walks the walk! Over 50% of our executive team at HireVue are women, which is quite rare in the tech industry. We were also named on the 2019 Shatter List, which recognises technology companies that are actively shattering the glass ceiling for women in technology, through its programs and culture.

There is currently only 17% of women working in tech, if you could wave a magic wand, what is the one thing you would do to accelerate the pace of change for women in the industry?

I’d give companies the strength to be bold! We know what tools promote competence and diversity- it’s time to implement! Trust the evidence- this will increase profitability. Formal selection, promotion, and pay processes. Flexible working hours and mentor networks. Parental leave provisions that are equal for both genders, and support with childcare.

What resources do you recommend for women working in tech?

There are loads of great women that you can follow on Twitter, as a start! A couple of my recommendations would be @cindygallop and @NathalieNahai.

Another area of focus for me is competence – this is a great article on why so many incompetent men become leaders: https://hbr.org/2013/08/why-do-so-many-incompetent-men

Events are an important and easy way to meet likeminded individuals and discuss shared experiences – some of the best are run by Future Females.

Education is important to me – and that doesn’t just mean textbooks! It’s key to educate yourself on the history and actuality of feminism and equality. My starting suggestions would be:

Lastly, it’s important to keep a sense of humour… Laugh about it at @manwhohasitall.