We had the opportunity to interview Shenine Botes, Data Scientist at Superbalist.com, one of the speakers at Women in Digital & Data Cape Town running on 21 November 2018.

Can you share a little bit about what it is that you do and what a typical day for you is like?

I work at Superbalist in the Data Science team, my typical day is spent munging data, researching modelling techniques and doing machine learning to solve business questions through the power of cutting-edge technology. I spend a lot of time doing data engineering too, we work in GBQ and we manage our own data warehouse as a team.

What are some of the biggest challenges that women who want to venture into the world of data analytics/data science face today?

The biggest challenge is getting past the notion that engineering and/or technical work is for men. We tend to put a lot of blame on the men in the industry, but we often find ourselves doubting our own abilities. This is a result of the way that we have been raised and the cultural taboos passed on from previous generations. These views are outdated as the world is changing at an incredible pace. Men are no longer the “bread winners of the family”. We need to start with ourselves and adapt our own thinking in order to convey the confidence that come naturally to our male counterparts, this will help us get the jobs, the titles and equal pay.

Data analytics/data science is perceived as a male-dominated field. What steps should be taken to attract more women to the field?

Starting with the route of the problem, we can create outreach programs to schools, informing the next generation of women about the range of career possibilities for woman, including tech roles as viable options. Furthermore, offering bursaries to woman who choose tech degrees is an option. Other than that, woman in technical positions should hold their male counterparts responsible during periods of hiring. 

• Keeping diversity top of mind in the workplace is important, and it is possible to do this in a professional manner.
• Can you pinpoint one moment or person that was instrumental in your decision to pick this career path?
• Not one single moment, I was born with an affinity for mathematics, technical and practical work. I wasn’t interested in the more ‘feminine’ fields of study. As a kid I preferred to follow my dad around the house, helping with whatever he got up to, as opposed to spending time with my mom in the kitchen.

What advice do you have for anyone interested in a career in data analytics/data science?

• Become tech savvy, do your research, learn SQL and Python and solve problems on Kaggle. This will be a very good introduction to Data Science!
• Do you think that data can help build a more diverse and equal workplace? How so?
• Yes, I think we can create a less biased hiring process by basing our candidate choices on skills and apathy as opposed to pure preference. By basing our decisions on tangible data points, we are able to remove any bias from the system. We can develop questionnaires that will highlight good candidates, these questionnaires can look at hard skills, soft skills, personality traits etc, thus removing the focus from the way in which a person is perceived from a social stance.

What do you think is the best part of being a woman in the data industry?

I like the shock factor, when people ask what I do they are normally very surprised at my answer. It gives me the feeling of being a trend breaker, and I’ve always been very liberal.

I like being a good teacher. I find that junior colleagues sometimes prefer to ask me questions as opposed to asking my male counterparts. I think it’s possible that woman are perceived as less threatening and more patient when it comes to teaching.

What are some of the best and worst workplace initiatives you have seen/heard of to help promote diversity?

Best: BEE

Worst: no initiatives at all

What advice would you give to a woman considering a career in data/data science? What do you wish you had known?

Don’t try to understand everything before applying for jobs, the best way to learn is on the job. I wish I moved over to DS from Analytics many years sooner. If you can’t find a job in Data Science right off the bat, look at other jobs like Data Engineering, BI and Data Warehouse Management but keep learning about coding and ML in your spare time and keep applying for the roles you desire.

What do you think we should be doing more of to encourage more girls to consider a career in data analytics/data science?

Educate them on the importance of diversity in tech, and make sure that they understand that tech is not just for men.

Educate their parents.

Bursaries

Equal Pay and equal opportunity is key too, no woman wants to be stuck in a dead end role where only men get promoted.

Any reading/website you would recommend in order to stay updated?

Medium (select Machine Learning & Data Science as topics for your feed)

towardsdatascience.com

Kaggle – take part in challenges and competitions, build a portfolio

stackoverflow.com

Fun fact about you?

I’m a crazy plant lady 

 To hear Shenine speak at Women in Digital & Data Cape Town book your seat here