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Finding Your Space: Three Tips for Women in Data Science

Coined by the Harvard Business Review as the ‘sexiest job of the 21st century, ‘data science’ still feels like a term invented only yesterday (and ‘women in data science’ seems even newer). And what does a data scientist do anyways? You could ask 100 professionals and get about 100 different answers.  

I thought being a data scientist meant that you needed a very specific set of skills – and a PhD – but that’s not really the case. I believed that by working as a data analyst I was missing the “science” part. Low confidence and impostor syndrome meant that I struggled to contribute to meetings. I thought others were more qualified than me and would let them take the lead on projects. This became worse when I joined a team where I was the only woman. With no one who looked or thought like me, I couldn’t feel more out of place and considered leaving the field. 

Careers in data are not a one size fits all and fixating on the long list of skills you don’t have won’t serve you well. Diversity is key to the success of an analytics team, and that doesn’t stop at gender or race. It includes diversity of thought, skills, and life experience.  

If you’re struggling to find your best fit, especially if you’re a woman working in data science, then here are some tips to help put you on the right path.

Find support 

We tend to underestimate the importance of mentors, role models and advocates in our careers. All are important to find fulfillment and to progress. Mentors will bring a different perspective; they’ll help you see things for what they are and overcome challenges. Role models will show you what’s possible, what you could aspire to and potentially, how to get there. Advocates will help others see how great you are. 

I reached out to women working in the field and found a couple of amazing and inspiring mentors, like Victoria Pike, principal product manager at Sainsbury’s. She helped me understand that my differences are an asset and that I should shift my focus from my weaknesses to my strengths, and Lucy Whittemore, vice president of Retail Partnerships at Cardlytics’ UK office.  

Thanks to this group, I started finding my voice and rethinking my role. From that point, everything changed. I could finally be myself and do what I did best. 

Get to know yourself better 

Often, we go with the flow, jump from one role to another wondering where is this sexy data science career that was promised. Well, you won’t find the perfect role unless you know what that looks like for you.  

Think about your values, what you stand for and what your strengths are. How does that translate in your role and how can you make better use of your strengths? What would you ideally be doing on a daily basis? 

Find areas where you can make a difference 

For me, it’s bringing transparency to careers in data through podcasting, but also by supporting data professionals in their career development.  

What is it for you? 

In data, there is a space for everyone, and that space might not be what is written on your job description. Fortunately, data science is a constantly evolving field, offering the best opportunities to try things out so that we can craft our own path.  

If you’re interested in pursuing a career in data science, then check out the open positions at Cardlytics

Karen is an analytics consultant in the Cardlytics UK office.She hosts her own podcast, Women in Data, and is the co-chair of Cardlytics’ Women of Cardlytics, with the mission to create an inclusive community committed to uplifting women in technology and establishing forums to drive connection, education and collaboration within our workplace.  

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Cardlytics Research and Insights
Blog

Finding Your Space: Three Tips for Women in Data Science

Coined by the Harvard Business Review as the ‘sexiest job of the 21st century, ‘data science’ still feels like a term invented only yesterday (and ‘women in data science’ seems even newer). And what does a data scientist do anyways? You could ask 100 professionals and get about 100 different answers.   I thought being a data scientist meant that you needed a very specific set of skills […]

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