What it’ll take to get more women into data science

Welcome to the fourth 84.51° Data University blog, a series of quarterly insights for prospective and current data science professionals.

Our experiences in the workforce and as consumers are increasingly being shaped by data. Data scientists, data analysts and other professionals working in STEM roles are critical to helping companies make informed decisions and move their digital ambitions forward.

Data scientists are in high demand—employment of data scientists is projected to grow 36% from 2021 to 2031, “much faster than the average for all occupations,” reports the U.S. Bureau of Labor Statistics. Despite the importance of these roles, only about 20% to 24% of professionals in data science-related roles are women, according to various surveys.

At 84.51°, women hold data science and STEM roles in different levels across the organization, including senior leadership roles. Find out what four senior data scientists say is needed to close the gender gap in data science and what they find exciting about their careers.

My entry to data science happened somewhat serendipitously! I was a marketing major in college and I decided to pick up another major that was relatively new called analytics. I loved the logical and problem-solving nature of analytics and also loved being able to incorporate the “human” element from marketing. To me, that is data science in a nutshell – solving complex, meaningful problems with an end user in mind. The field has evolved and grown from when I first became a data scientist. Every role I’ve had at 84.51° in almost six years has been unique and brought a new set of challenges and experiences – being able to try something new and continuously learn is exciting!

I consider myself an insights data scientist, meaning I specialize in building data science products that allow users to gain meaningful insights. I’m a member of our Assortment Insights and Delivery team which is dedicated to delivering insights to Kroger and CPG stakeholders who are making assortment decisions in Kroger stores. I find this role exciting because the results are so tangible – the recommendations and insights our team drives has a direct impact on Kroger customers and we are constantly aiming to improve that customer experience. It’s also fulfilling to know that our products are integral to teams across Kroger – this means we are motivated to constantly improve and make processes more efficient.

I believe that visibility encourages more women to join, or remain in, tech and data science. Knowing that there are other women out there having similar experiences as me has always made me feel part of something greater, and I have a wonderful cohort of women data scientists and technologists at 84.51° who constantly inspire me to be a better data scientist.

I remember reading an article in high school about a retailer using customer data to send out targeted advertisements. I was really fascinated by this concept and would think about it every time an advertisement got me to buy something.

Fast-forward to my sophomore year in college when I was an actuarial science major. I heard about a new data science program starting up and decided to take the introductory class just to see what it was about. I ended up loving how it incorporated math and problem solving to make each task feel like a complex puzzle. Next thing I knew I was switching my major to data science and eventually adding a computer science major as well.

I’m a senior data engineer at 84.51° and work mostly with ingesting advertising data from different partners and providing it for business user consumption. The thing I enjoy most about my role is when I get to work on a pipeline/project from end-to-end and see it grow from architecture meetings to production deployment/go live.

I think it needs to be advertised to young women that the only requirement for taking an introductory computer science course is a strong foundation in math. I remember there being a computer science elective at my high school, but I was aware that everyone in that elective was also very interested in video games. This idea was also perpetuated through media where every character that worked in tech also played video games. Due to this I thought of being interested in video games as a sort of requirement for going into the tech field. It wasn’t until college when I realized that playing video games was just their channel into the tech world, it wasn’t an interest required to succeed.

My journey in data science officially started during my Masters program back in 2018. I went to University of Cincinnati for Masters in Information Systems. Very much like my peers, I also questioned what career option I wanted to choose. Data science was and has been a booming industry so it was definitely something I considered but my goal was also to evaluate if I liked doing it.

Much thanks to my program as there were many great data science courses to choose from. I took a lot of data science courses at grad school, e.g., deep dive in machine learning, data mining, learning data visualizations etc. along with the regular course track. This is when my interest peaked as I realized how powerful this domain is and how almost every industry is trying to incorporate more data-based decision making.

I think it’s really important for us to not just do our jobs but also realize the impact it has. I was able to see it first-hand, the power to crunch billions of rows of data and derive a single insight and help drive the decision making at an organizational level. Besides, I also really have enjoyed the process of learning new tools and techniques and just amalgamating it with the business results.

Currently my role at 84.51° is primarily focused on descriptive and prescriptive analytics which is looking at the trends in the data and highlighting key focus areas for the business. My role as a data scientist involves automation of insights and building new tools that enable us to do so. What I really enjoy about my role is that there is so much to learn every day whether it is adapting to a new tool/technology or learning to shift focuses with the changing industry trends to enable the business.

I think it’s not just about promoting data science jobs among women, but recognizing diversity is also very important. Gender is probably the first thing that comes to our mind when we think about diversity amongst others. I think realizing this gap and educating not just women but everyone by incorporating the right trainings on gender biases attached to recruitment process, promotions, salaries etc. is really important. Another important thing is also encouraging young girls to pursue more careers in STEM in primary school as the interests may be more set by the time we reach college.

I was a math major and was talking to professors about my career after school. I expressed that I was not interested in becoming a teacher or pursuing higher degree in pure math. Instead, I wanted to go into medical research field (thought that was my passion back then). One of my professors recommended changing my focus to applied math since there was no statistics department at the time. It was how I was introduced to R and SAS and led me to the data science field.

I enjoy translating technical/statistical methodologies to stories for non-technical people. In Marketing Analytics and Insights (MA&I team), I often designed and/or measured campaign performances. It is critical to create effective visualizations to explain propensity score matching for test and control groups, time alignments among different channels, statistical significance to stakeholders who may have none to very little statistical/technical backgrounds.

I would say awareness, advocacy and mentorship. These were the key areas we wanted to focus on building when Jyostna Bernet and I established Women in Tech (WiT) back in 2019! Most tech fields are very male heavy, and we wanted to have a group for all women technologists to advocate for each other.

I think it is very important to educate everyone about challenges that women technologists have to deal with. When we asked women technologists what challenges they were faced with when they started their careers, the main theme was ‘frustration’ and ‘unheard.’ I have been trying to connect with other women data scientists in the organization, and help them recognize their strength and build their technical brands.

I think having women mentors is very important, too. We need more women that are successors and can mentor junior women technologists when they enter a career field. In WiT, we established a program called ‘Peer Mentoring’ that pairs women technologists by their short-term career goals regardless of their function or tenure.

We also volunteered to mentor elementary, middle and high school girls to talk about STEM fields. We focused on highlighting some of the skill sets that we have and how we got to where we are.