Team Diversity: Women In Data Science


Paul Derstine

Date Published:
June 15, 2018

According to Women in Tech: The Facts, a report by the National Center for Women & Information Technology (NCWIT), “In 2015, women held 57% of all professional occupations, yet they held only 25% of all computing occupations.” The NCWIT report authors believe that this pattern is “especially troubling given ample evidence of the critical benefits diversity brings to innovation, problem-solving, and creativity. Indeed, a solid body of research in computing and in other fields documents the enhanced performance outcomes and benefits brought about by diverse work teams.”

The U.S. Department of Labor in 2016 reported that the percentage of computing occupations held by women peaked at 36 percent in 1991 and has been mostly on the decline ever since.

An infographic from Why We Need Women in Data Science shows a similar number as the NCWIT report — that only about a quarter of “data professionals” are women.

According to Women in Data Science: 4 Perspectives “recent data suggests that these figures are changing, though. In statistics, women are a growing force. More than 40 percent of degrees in statistics go to women.”

Are women encouraged or discouraged to enter into and to thrive in the interdisciplinary technical field of Data Science? To get some additional perspective from within our own ranks I interviewed several of our female data scientists to get their take on working in the field.

Why did you choose a career in data science?

Halee: Data Science is an exciting field with the opportunity to solve new challenges and help businesses and clients improve their processes. There are technical challenges that satisfy my curiosity to continue learning while providing me with a very fulfilling career.

Jennifer: I developed an interest in data analysis in my previous work life, and a data science degree gave me the ability to easily pursue my new interests.

LeAnna: I liked the idea of being able to use math/CS to solve real world problems, versus the theoretical use cases for math. I enjoy solving the puzzles of coding and applying math to solve a problem.

Anna: As a medical device product engineer, I had been using data collection and analyses processes to solve product development challenges. Being able to use data to justify development or process changes resulted in a better product. This was a rewarding experience that developed in me an analytical skillset and mindset on the job. Upon learning more about data science and analytics as a career, I decided to go back to school and make the switch.

What challenges have you faced as a woman in the field?

Jennifer: I supposed to it depends on the make-up of the work environment, but in the offices or companies where the staff is predominantly male, it can very much feel like a boys club. Discussions about common interests lead to friendships and collaborations that are harder for a female to establish.

LeAnna: Throughout my career it has been difficult to get my peers to trust my skill set. My work is often initially met with doubt. Two examples. 1) After submitting a code rework that implemented abstracted functions to improve the modularity of a script, I was asked who helped me write my code. 2) A peer who admitted being rusty in a coding language was trying to debug their code. When I made suggestions, they were disregarded — even though the final working solution used the changes I had suggested.

Do you believe that opportunities are increasing for women in data science?

Halee: Yes, data science can be applied to many different domains so there will always be new opportunities and an increase in demand for new data scientists. Increasingly, a large portion of data science jobs tend to have a consulting component. When starting new projects, many begin with a business understanding phase to determine objectives, goals, and create a project plan. Having a mix of women and men on the team provides the opportunity for a diversity of alternative viewpoints that comes from different life perspectives or experiences during these project phases.

What aspects of your work do you find most rewarding?

Halee: Helping clients see value from their data is very rewarding. Data Science and analytics can have real influence on arming a business to make informed decisions and provide a positive impact on the outcome. Cleaning training data, also known as “wrangling”, is where the data scientist spends the majority of her time. I thoroughly enjoy wrangling raw input data through an ETL process to transform it to a format suitable for modeling even thorough some would regard this as a the “less glamorous” part of data science.

LeAnna: I appreciate knowing that my work is able to help improve the daily lives of others — big or small. Whether that be small wins such as improving customer experiences, or larger examples such as helping improve medical diagnoses.

Jennifer: Elder Research is a fantastic place to learn and experience new types of projects, I’m thankful every day that my job isn’t stale.

What do you see as the most exciting opportunities for the future of data science?

Halee: AI and Deep Learning. As processing power increases it lowers the time it takes to utilize this technology. There is a lot of excitement and new opportunity in this area.

Jennifer: Personalized data analysis, such as medicine, optimal career paths, etc.

LeAnna: The opportunities in medicine are very exciting to me. Being able to catch diseases earlier, or accurately diagnose a patient has huge implications.

Anna: I’m interested in seeing how data science will continue to influence the increased interaction between humans and computer-driven agents (think Alexa, automated driving, and so on). What new services will be created? How will this affect me over the next 10 years? What are the ethics at play? What eventual policies will be developed as a result? It’s a really interesting time as data science, artificial intelligence, and automation become more pervasive in our day-to-day life.

What advice would you offer to women thinking about pursuing a career in analytics?

Halee: It is important to be active and connect in the data science community, to be exposed to new opportunities, and to stay abreast of emerging technology.

Jennifer: Be aware of the gender (and ethnicity) ratio prior to starting, both in the workforce and in leadership. If it’s unbalanced, is leadership aware? Is there an explanation? Are they taking steps to remedy it if it is a problem? Working in a (non-discriminating) predominantly male company may not be a problem for some women, but it may be a huge problem for women who are technically savvy, but shy or have trouble finding their voice.

LeAnna: You’ll face doubt and adversity, but if you’re passionate about the work, stick with it. The reward — when being able to solve a tricky problem, or discovering new findings — is incredibly satisfying. Also, the easiest way to eliminate the doubt is to let your work prove them wrong!

Anna: Find a woman who works in data science and ask her questions! Chances are, she has been in your shoes at some point in her career, and she will be a valuable resource for you as you navigate the ins and outs of pursuing data science and analytics.

Any additional thoughts you would like to add?

Halee: I transitioned to data science from a non-traditional path and I would encourage other women to not let a non-traditional background stop them from pursuing a career in analytics or data science. It has been a very rewarding career change for me and the field and opportunities only continue to grow.

Diversity on a project team—gender, skills, experience—enriches the creative process and adds depth to analytics solutions. We celebrate the contributions of women and their impact on data science, their courage to challenge bias, and their determination to fight for opportunities and achieve advancement.

Interested in this topic?

Read more about this topic in our Women in Data Science white paper.
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