With only one in six data professionals being women, Samantha joins Unlimit to discuss how we can encourage a more female future in the field. Data science is not only thought led and formulated, but it offers the opportunity to experiment and draw a consensus from learnings. The cognitive ability to juggle, use informed rationale and beyond make the STEM field a great fit with these natural traits of women. We’re joined by Samantha, from Kantar, who brings us insight for young professionals and even advice for corporations to promote the presence and growth of females in the space.

Please start us off with a bit about your career journey and learnings to date, and for those who don’t quite understand what a data scientist does, please grace us with a short explanation?

I am currently based in Amsterdam, but I originally come from a small country in Africa called Malawi where I was born and raised. I moved to South Africa at the age of 16 and studied through university there with the aim of becoming an aerospace engineer. But at university my mind changed because of the diversity of experiences I encountered, which was when I decided to graduate as an industrial engineer.

And I started my career with one of the big four consulting companies called UI. So, I was a management consultant for the first four or five years of my career, and that’s when I really got into data science and became a supply chain analyst. I took part data science competitions and learnt to program whilst I was there and during my spare time. It was during this period that data science became a clearer pathway for me as I was self-taught, but it wasn’t something I had considered as a career. It was only later that I decided that I wanted it to become my career and took on the challenge of doing my master’s at UCD, which accelerated my path to where I am today at Kantar.

In terms of what Data Scientists do, you can often hear differing versions from people, because the roles are still relatively new and not so defined. The role really came into organisations in the 2010s and derived from where computer science met data analysis. Data science is about using scientific approaches to make an informed decision about the data which is collated. This then allows business to formulate data-driven products in an intelligent way and helps to advance methodologies. It’s a remarkably diverse role, which requires programming knowledge, as well as data interpretation, and even a coherent knowledge of what the organisation (who are applying the science) does. Being a Data Science Tech Lead is a more involved role because you go beyond collecting the data, to using it to solve complex issues and topics.

You must know a lot about the business as well. Domain knowledge and stakeholder communication and statistics. So, it’s a little bit more involved from my day-to-day job as a data analyst. And I really loved it because it showed like the promise of solving complex stuff that couldn’t be solved before traditional data. And now like there was new stuff with more complicated applications like being sample data science. Things that we all take for granted like Google translations, would not be possible without the use of data science processing information on languages and programming speech to text via a computer.

What is a normal day in the life for a data scientist at work?

From my experience as a market research data scientist, my day is dependent on whether I am working on a new project or maintaining an existing application. One of the main things for me as a lead is to ensure that I spend enough time with my team, so I like to spend my morning catching up with them, seeing what they are working on and so forth. In the role, you work on a couple of projects at a time to juggle. The projects would either be based on research and development, or it would be a methodology in which you’re trying to explore new ways of using the data that we have in authorisation. Another example is an operational type of project which involves improving and automating some form of business versus an organisation, whether it’s data retrieval or exploration that may use unsupervised methods.

There are times where your day is taken up with supporting a project where the products and solutions have been agreed on. In that instance you are juggling between the developers, data engineers, product owner, etc. to make sure the solution is in production and that you are monitoring the process to fix any bugs and troubleshoot potential issues. Lastly, there is a constant flow of managing ad hoc requests and communications, whether they are internal or external with stakeholders to update them on the processes and stages that the projects are at. Surprisingly, this is one of the most challenging parts because you must learn to push back and identify ways that are most useful to handle different personalities.

How did you enter the field, and do you feel that your experience as a female has moulded your opportunities?

Well, as I started off my education as a mechanical engineer, I saw how few women there were in my class. Specifically, I was the only woman. Occasionally there would be some women, but they were only there temporarily, and I did feel lonely. So, when I switched to industrial engineering, part of that decision was driven by how lonely I felt. I found that in this switch, I was able to be around other women and felt mor at ease, and even met my partner in crime in the course. We saw our degree through together and having her around me was so empowering, because we could learn from each other and support each other. I think that it’s important to remember that people often think it’s delusional to think you can’t be empowered by only men, as though we don’t need empowering women. But the reality is that no matter what, we need to see a familiar face in our surroundings to feel stronger and empowered. Being around other women really moulded my degree and my approach to entering workspaces, as well. When I continued into my master’s in data science, I entered the class as the only woman again but this time I had a sense of maturity and confidence about me that wasn’t going to be shaken by the people around me. It was a great feeling graduating as the only woman in my class and I tried to take that feeling into my jobs where I learned to survive and behave in a male dominated environment.

When I did begin to feel that I was really missing that female presence, I started engaging with women in tech communities or females online on which I could lean. Surrounding               myself with those individuals helped keep me motivated. But all of that aside, I really do love what I do and my career. Having a community of sisters and friends outside of work just helps keep me passionate and gives us all a space to bounce ideas off each other. No matter what position you go into, it provides its own challenges and that’s something that you need to prepare yourself for.

What do you love about working with data and as a Data Science Tech Lead?

I have always been in love with data visualisation, where you take information, and it becomes this beautiful spreadsheet of data and logistics. You see a spreadsheet that starts off with all zeros and then eventually it is filled with information and insights that are beneficial to businesses, which is so exciting. It’s a creative process, and I don’t see it as purely maths and science. I’m a creative person as well, who enjoys making things look beautiful, so when I got into Data Science, I worked on projects using things like nitric blots and metric graphs to visually uplift the data and make it more appealing.

It’s amazing how you can explore these dashboards you create using different skills and tools such as text mining, unsupervised learning, and machine learning. It’s an industry and skill that is so applicable to many areas of the world, which is so important. For example, I come from Malawi and when looking at how the community is disadvantaged, there are numerous applications which show how to improve and scale agriculture and food. The social data science from machine learning could help the community evolve and better understand elements of weather predictions to improve agriculture and food supplies, and ultimately their economy.

All these opportunities are what truly inspire me and empower me as a Data Scientist, but also to even process my own projects outside of work too. It’s not an industry that is limited to 9-5, but there are so many ways of getting involved in it as a hobby too.

STEM is so intertwined – how has technology evolved the learnings you have been able to find and explore?

I think it’s really changed a lot, which is shown in the simple fact that I began my studies through Excel. Now there’s a wider need to adapt and learn to the new world of open-source technologies and what do with accessing bigger segments of data. The internet alone is a huge data hub for us to explore now, and we can engage with communities and use sources like Kaggle, courses and free information.

So, I think that the technology landscape has changed a lot, and so have the available cloud platforms. I’ve even seen how there is a demand for technological understanding in careers that are further removed from Data Science, such as Product Manager roles, where employers want the candidates to learn and understand sequel and python. Ultimately, technologies are shifting everything we understand, to a place where we can realise and learn a lot more and take better value from the information we can obtain.  

A Zippia study online in April 2022 showed that 20% of data scientists are women and 80% are men. Do you think that there are areas of the scientific space and community that would benefit from a stronger presence of women? Why do you think that the area is lacking female presence?

Truly, I think almost every area would benefit from a larger presence of women. The presence is still minimal in STEM fields and Data Science, which is where I feel most comfortable speaking about. I recently read a book called Data Feminism, and was thinking about how I have become a mother too and the issues that women face then felt more personal and closer to my heart with motherhood. The book comments on how women in the Data Science field have now entered a conversation and how the data can be used to provide better information for women with maternity and issues that directly impact women.

And when I read this book, it highlighted the link between, let’s say, people recently learning more about how breastfeeding is amazing and it’s good for your child, and why. It’s only because women who’ve entered the STEM fields have now dedicated time to do research in these types of areas. There’s lots to be said how men wouldn’t be interested in data like this, because it’s not personal to them which is why there is a need for women to come in and provide that data. I think that the only way that we can really increase the drive among other women to join, when they see other women who are in the field and talking about subjects that women are passionate about.

There are numerous topics that still come up as ‘not having enough data’, particularly in areas that directly correlate to women. There is a lack of studies that can correlate different topics, and this could be remedied by having more women in STEM and Data Science who can analyse these gaps. The information wouldn’t just be a benefit to women, but also to disadvantaged communities where less studies are held in the world.

How can STEM companies get more women and girls excited about finding their career in data?

We do need more organisations identifying research areas that need to see impact and diversify it from the focuses that we currently see like self-driving cars or the metaverse. The metaverse is not necessarily something that will really engage masses of young women or women to want to enter tech. The scientific space can make it more engaging for women and girls to join the space, so for those who aren’t interested in these topics at hand, we can showcase how there is so much science, data, and chemistry in the world of cosmetics. So, the data involved in the beauty industry is something that people can explore and feel more engaged with in a topic is in their daily lives.

We can’t assume everyone will love cars or cosmetics, so it’s about showing how application of technology and Data Science can be more relatable to things that people enjoy. Women being in the field will help broaden that depiction into society because more areas will be researched and available for people to see.

Organisations would greatly benefit from uplifting women within their company, to keep female talent and showcase career journeys to younger team members to engage with the idea of growing within the business. And it also feels like there’s not enough space for women in STEM organisations, when a young girl comes in and sees little to no women in management, you can understand why they would question their future in the business.

It would be great if organisations and senior team members can repay their knowledge and experience back into the community. By increasing visibility of opportunities and potential avenues, people feel more motivated to pursue their careers. More people feel like they are a tick box of diversity and inclusion, which stops people from feeling engaged. The leaders of a business are responsible for executing plans that support people’s passions and show people how they can make a difference. They may just want a job and they have absolutely every right to. But I think even just them being there is also very symbolic and can help that person who walks through the door to see X number of women sitting around the office, or people of colour or of different ethnicities, and varying disabilities. You automatically feel more comfortable because don’t feel so alone.