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.