Women in data: Interview with a data scientist at Ceqence

What attracted you to a career in data science?

With a background in economics and econometrics, I already came from a numerical subject and had used statistical modelling in my work. But I found the coming together of increasingly abundant data, the increasing availability of cheap computing power, and the way those two forces enable us make so much quicker, smarter and impactful decisions very intriguing.

It’s that shift from explanatory to predictive modelling that opened my eyes to the wealth of opportunities for enhanced decision-making and made me pivot into field of data science.

What’s been the most memorable moment in your career to date?

Realizing that most companies don’t understand the function of a data scientist or the vast potential of data science in their organisation that is yet to be tapped into.

Male analysts and scientists outnumber females 4 to 1. What advice would you give to businesses struggling to attract and retain senior female talent?

It’s interesting because most companies I’ve worked on data science teams in have grappled with the lack of female talent and also made a conscious effort to prioritize the issue. Companies are keen on getting this right but it’s hard. Thankfully there are great options for tipping the balance.

A lot of research has been done about how best to recruit more women to companies, resulting in recommendations such as making job descriptions more inclusive by paying attention to wording, highlighting success stories of women in the organization or having an explicitly inclusive work culture and talent brand.

I’d add in flexibility in terms of working hours and remote working, which gives women more options for childcare arrangements, as well as a team atmosphere where this flexibility is encouraged (as opposed to glorifying the long-hours culture).

Finally, working together with gender diversification platforms whose aim is to close the gender gap in the space of data science and analytics, and that exclusively focus on plugging female data professionals is another great option for companies.

What excites you most about recent developments and the future of data science?

There are lots of current AI developments I am excited about but most recently it is how the application of deep learning in the field of healthcare can revolutionise diagnosis and treatment of diseases. That makes it possible, for example, to mimic the human ear by converting spectrograms (sound data) from a digital stethoscope into a computer vision problem for instant detection of respiratory diseases without requiring the expertise of a medical doctor. These developments have particularly huge implications for people in less privileged parts of the world.

What advice do you have for any females considering a career in data science?

Just do it. It’s challenging, it’s rewarding and best of all its fun!