Women in Data Science

Featuring Jane Poole (ILRI – ICRAF)

Jane Poole

Jane Poole

Statistician (ILRI)

At ILRI I provide support to researchers on their study designs and analysis, I also lead a support group which includes staff who support data workflows from collection in the field through to databases and visualization.

How did you come to pursue a career in data science?

I’m an applied statistician so I didn’t even realise I was a data scientist until I googled it.

I found this comment by Nate Silver to be particularly interesting: “Data-scientist is a sexed-up term for a statistician….Statistics is a branch of science. Data scientist is slightly redundant in some way and people shouldn’t berate the term statistician.”

However, I see statisticians as a sub-group of data scientists but I’m not an expert, or even a novice, in many of the key areas of data science such as machine learning and bioinformatics. I started my journey towards statistics with a mathematics degree and worked out towards the end of my degree that I wanted to work in Research for Development and looked for opportunities to apply my skills in this area. That led me to an MSc in applied statistics with an internship working for ICRAF in Nairobi.

What are the things you love about your role as a data scientist?

I love working with researchers from different disciplines, understanding their research and development challenges and using my skills to help them to design and implement activities to respond to those challenges. I am sometimes envious of researchers that they can focus on their interest area but I like the fact that I know a lot about different aspects of our work at ILRI and can make connections across research areas and programs.

What has been the most exciting project you have worked on?

I really can’t pull out the most exciting because it would be unfair to other projects and organisations I’ve worked for. I have been privileged at ILRI to support projects ranging from livelihoods, food security and nutrition health through to livestock breeding programs. From a technical perspective then I particularly enjoy the design and analysis challenges of our longitudinal studies, such as monitoring livestock performance over time or epidemiological outbreaks of diseases, because this is where the designs can be complex.

Do you feel this is an exciting time to be a woman working in the data sector?
I think it’s an exciting time for anyone working in the data sector, as there are new methodologies and tools appearing every day and organisations both public and private recognizing and investing in people with these skills. Although, I have to admit that I find the level of information and data out there a little overwhelming and its challenging to keep up. I also envy those data scientists who get to work in narrow areas of application where hopefully they get to prioritise a smaller number of innovations.

Why do you think there is a lack of women in Tech/Data sectors?

Tricky question. Probably for lots of reasons. I guess in some contexts there are barriers to women joining these sectors from an education and cultural perspective, and in other cases perhaps the sector doesn’t excite a lot of people. To be honest it wasn’t until I was in my late 20’s that I even realised there could be reasons why some sectors have more women than men or vice-versa.

As a woman, did you face any obstacles/challenges in your career pathway to become a data scientist? Any advantages because of your gender?

I would say no for both questions. I grew up in a very gender-neutral family, my mother went to university to study mathematics and physics in the 1960’s in UK and worked as a high school teacher in physics before, during and after having children. At home, it was generally accepted that Mum was more intellectual and had a more important job than Dad who was an agricultural civil servant. Home chores and childrearing were equally spread between them.

I think how we raise our children is a major contributor to what they believe they can do with their lives and careers as well as other aspects of their lives. Perhaps my upbringing made me blind to inequalities and differences in opportunities.

Why should more women get into data science?

I wasn’t pulled towards data science for the ‘data’ but for what information and knowledge the data could give us, and my interest is in problem-solving and extracting that knowledge. I think that a lot of people may be attracted by that aspect of data science.

How could more women working in data, specifically for the development/agriculture sector benefit the sector?

Organisations, sectors, and disciplines are always going to work better with a balance of women and men, as well as other aspects of diversity, because we approach problems in different ways, ask different questions and to achieve sustainable development we need people thinking in different ways towards a common goal. I think data science is the major contributor to helping development decision-makers to make informed decisions, so I’d encourage anyone to get involved.

What advice would you give to women wanting to get into data science?

There are so many sub-groups of data science that you can be overwhelmed with where to start. There are now courses called ‘data science’ which could give a broad introduction. I would advise to look at what interests you – it could be statistics or dealing with big data – such as data mining or genomic data – or making data understandable and digestible (visualization), or even the programming side.