Women in Data Science Worldwide

Data scientists are involved in a wide array of domains, everything from healthcare to cybersecurity to cosmology. Megan Price and her colleagues at the Human Rights Data Analysis Group (HRDAG), however, are using data science to help bring human rights abusers to justice.

The nonpartisan group played a key role in the case of Edgar Fernando García, a 26-year-old engineering student and labor activist who disappeared during Guatemala’s brutal civil war. Price, the executive director of HRDAG, says the investigation took years, but their work led to the conviction of two officers who kidnapped Garcia and the former police chief who bore command responsibility for the crime. “It was one of the most satisfying projects that I’ve worked on,” she says. Price discussed the case in more detail as well as other cases she’s worked on over the years and the role data science played in an interview recorded for the Women in Data Science podcast recorded at Stanford University.

For a recent project in Syria, Price’s group used statistical modeling and found information previously unobserved by local groups tracking the damage caused by the war. Similarly, in Mexico, she expects HRDAG to gain a better understanding of in-country violence by building a machine learning model to predict counties with a higher probability of undiscovered graves.

Price hopes that in the future human rights and advocacy organizations will have their own in-house data scientists to further combat social injustices around the world, and she believes that data science will continue to play an important role in the field. She advises young people entering the field of data science and social change to learn a programming language, pick an editor and find mentors and cheerleaders to help them along the way.

When Jennifer Widom began her career in computer science, it was a relatively narrow and specialized field. Three decades later, computer science has become an interdisciplinary field that touches on broad swaths of society and promises solutions to global problems such as healthcare and sustainability, she says. “Computer science used to be a niche. But (it) has become much more broadly used, broadly applicable across all fields. Instead of it just being a narrow study of software and hardware, it's now a lot about what you can use that software and hardware for in other fields,” says Widom.

Indeed, learning about the relationships between math, computers and music prompted Widom to make a radical career change. Her undergraduate degree is in music, and she was on a path to become an orchestral trumpet player. But a course focused on computer applications for music was so intriguing she shifted her studies, eventually becoming a computer scientist and the dean of the School of Engineering at Stanford.

Increasingly, jobs in industries related to computer science will be broader and encompass the need for data science at its core. “We’ll still need straight-line software engineers, but there will be more jobs for people with additional skills and interests,” Widom said in an interview recorded for the Women in Data Science podcast at Stanford University. That shift may well make the field more attractive to women, she says.

Computer science has become so popular that nearly 20 percent of the student body at Stanford is majoring in it, and the university is struggling to keep up with demand, she says. Data science continues to play an important role in its continued evolution as more and more students use data to solve complex problems. But what do those students really want? “Are the students who are coming to computer science coming because they want to learn just the computer science, or are they coming because they want to apply computer science to their other interests? I'm going to venture a guess that the second is true for a lot of those students,”Widom says. If that’s the case, Stanford and other universities will need to shift the computer curriculum to be more reflective of its newly interdisciplinary nature, she says.

Widom pioneered the use of MOOCs —massive open online courses —and says teaching them “was one of the most invigorating and exciting things I think I've done in my whole career.” The experience of reaching so many people —her first effort attracted 100,000 students —inspired her to take a sabbatical in which she traveled to under-developed countries offering free short-courses, workshops and roundtables, covering such topics as big data, collaborative problem-solving and women in technology. Her “instructional odyssey” was not only personally gratifying, but it shaped her teaching. “I think, based on my experience with the MOOCs and travel, that the way I could best influence people directly would be to show up and teach them,” she says. “I just really loved reaching people all over the world.”

Leda Braga is the founder and CEO of Systematica Investments, a hedge fund that uses data science-driven models to support its investment strategies. Leda was born and raised in Brazil and found her way into the financial sector after getting her PhD in engineering and spending several years as an academic.

Her financial career started with seven years in investment banking at JP Morgan and then she joined the hedge fund startup BlueCrest in 2000. She explains that while her funds did very well during the 2008 financial crisis, the time felt like an existential crisis because you didn’t know if the major investment banks were going to survive. But she said it was a formative time and she learned many lessons. Several years after the financial crisis, she spun off her own firm, Systematica Investments focused on systematic trading.

Leda explains that systematic investment management is data science applied to investment. The systematic approach makes the investment process less reliant on the random nature of forecasting and more reliant on risk control in portfolio construction.

Both discretionary traders and systematic traders are looking at information to try to make decisions. Those who do it on a discretionary basis tends to look at the data and make a decision to make money on a trade. Those that look at data on a systematic basis build data-driven processes for trading strategies for certain risk profiles and preferences that will produce consistent returns over time. She says the responsibility weighs heavily on her to ensure a good return because people's pensions are part of the money her firm manages.

While she believes strongly in the power of leveraging data science in investment, we’re not yet at a point where AI allows us to do “autonomous investing” because there's a large element of randomness in markets and relatively sparse data so learning algorithms have limited use. She says that the only way it might be possible is if you’ve compartmentalized and narrowed the scope to the extent that you have a controlled amount of randomness. Learn more about Leda and systematic investing in her 2018 WIDS presentation, When Data Science is the Business.

RELATED LINKS
Connect with Leda on LinkedIn or Twitter
Find out more about Systematica Investments
Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn
Find out more about Margot on her Stanford Profile
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Today Veronica Edwards is a senior data analyst at Polygence, though her educational and career background encompasses a wide range – she has delved into everything from dance and choreography to physics, sociology, marketing, and most recently, data science.

Polygence is a nonprofit that offers middle and high school students a 10-week research experience under the guidance of a professional mentor. As a senior data analyst at Polygence, Veronica uses data to help build and scale the company and to provide students and mentors with an optimal experience.

Upon working at Polygence, Veronica was surprised to learn how little high school students are asked to do independent research. Independent research affords students the opportunity to explore their passions, get comfortable with the ambiguity of the research process, and become experts on their chosen topic. Polygence aims to democratize this research experience and has successfully targeted a diverse selection of program participants, attracting mentors and students in over 100 countries with a near-equal split of female and male participants.

Growing up, Veronica trained vigorously as a ballet dancer alongside peers who aspired to be professional dancers, though she knew early on that she did not want to pursue a career in dance. Veronica believes her training as a dancer helped her build strength and perseverance that have served her throughout her career. Furthermore, the creativity she uses for dance and choreography informs her work as a data analyst, helping her to tell the story of the data she oversees.

Veronica entered Princeton University as a physics major and then transitioned into sociology, where she saw how data could be used to understand society. While attending college, she explored different career paths through Princeton’s connections with the public sector. This led her to multiple internships in public service, including a marketing internship at Community Access, an NYC-based nonprofit. Upon graduation, she was accepted into a Princeton P-55 Fellowship, which connected her with her first job out of college as an executive assistant at ReadWorks, a nonprofit that helps K-12 students with reading comprehension.

Veronica recalls a clear moment at ReadWorks that propelled her into data science. “The senior engineer was in the office one day and he asked me, ‘Veronica, do you want to learn how to pull data on your own?’ In that moment I didn’t know what SQL was, I had never heard [of] it before, but I said yes.”

Veronica sees her non-technical background as an asset in data science because it allows her to think like other people, particularly those without technical backgrounds. “I come from a non-technical background, and so therefore for me, I'm a step ahead of people who do have a technical background, in explaining data because I know what it's like to not understand what's going on in a chart, for example, or what a P-value is.”

When asked what advice she would give to herself 10 years ago, she says she would tell her not to write off subjects that she enjoys but isn’t the best at. “I was always decent at math and decent at statistics and pretty good at all of these subject matters, but I wasn’t the best. If I would have told myself back then [that] one day you’re going to have a career in data science, I would’ve been really intimidated, because that seems like something you need to have extremely high standards for.” Additionally, she would urge her younger self to be open-minded about her future plans, because in her words, “you never know what opportunities are going to present themselves.”

RELATED LINKS

Connect with Veronica Edwards on LinkedIn

Find out more about Polygence

Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn

Follow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)
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Bio

Huda Nassar is a senior computer scientist at RelationalAI working on building the graph algorithms library offered as part of RelationalAI's product. Previously, Huda obtained a PhD in Computer Science from Purdue University and was a postdoc fellow at Stanford's School of Medicine. Huda is also known for her "Julia for Data Science" course which had over 13,000 students and focused on Data Science methods including graph analytics.

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