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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.

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How can women break into a career in data?

Article by Caroline Carruthers, CEO Carruthers and Jackson 

binary code, data scientistAt DataTalks last year, an annual event which brings together hundreds of data professionals from all over the world and a range of different industries, someone came up to me to say how incredible it was that there were so many women giving keynote speeches.

It’s normal in the tech world to think of STEM careers as inaccessible to women and girls and, whilst there’s still a lot of work to do in the tech space, the world of data seems to be a bit of an outlier.

It’s normal for events like DataTalks to have a large number of women giving keynotes and, as someone who’s proud to have helped to foster a wide-reaching data community, I’m constantly amazed at how far we’ve come in the data sector since I began my career. But why is data seen as a much more welcoming, and much more accessible for women than many other areas in the tech space?

I think the number one reason is because there are so many different routes into the data profession. Unlike many STEM careers, you don’t necessarily need a tech or science background: the data community values the skills brought by those with backgrounds in the arts or the business world just as much as they value those with science or more technical skills.

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That doesn’t mean there are no entry requirements to a career in data, of course, but the key characteristic that defines whether someone is a good fit for a data role is one that doesn’t have a bias toward one gender or the other: curiosity. You can teach anybody about the technical side of data science or the importance of data governance, but you can’t teach curiosity, and that’s the making of a great data professional.

So, if you’re a curious person interested in a career in data, the only real barrier is self-imposed limitations. Whenever I go into schools to talk to young women who are interested in a career in STEM, I always tell them that the worst mistake they can make is to limit themselves based on someone else’s preconception of what they should be doing. Even as adults, we often self-impose limitations; we need to learn to challenge ourselves and to stop asking ”why?” and start asking “why not?”.

Data is an incredible, rewarding profession which allows you to work with the foundation of pretty much all of the technology and digitalisation that we take for granted in the world today, and all of the innovation that the world is working toward in fields such as AI and machine learning. If you’re a woman looking to break into what I consider to be the most exciting area of the tech space, you just need two things: curiosity, and the ability to ask yourself “why not?”