Women in Data Science Worldwide - Jennifer Widom | Math, Computers, & Music

Jennifer Widom | Math, Computers, & Music

10/19/18 • 20 min

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.”

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Caitlin Smallwood | Data-Driven Video Content

Be yourself” was just one of the many career tips Caitlin Smallwood shared during a conversation with Stanford professor and Women in Data Science podcast host, Margot Gerritsen. Smallwood, vice president of data science and analytics at Netflix, urges up-and-coming data scientists to explore “the avenues and nooks and crannies” of the discipline and avoid limiting themselves to the most obvious paths.

Smallwood is passionate about data-driven content and predicts that deep learning will continue to propel advances in applied data science in the future, specifically in the area of machine translation. It will take some time, she says, but machine translation would allow users to watch a movie or video and understand the subtleties of language and culture at a deeper level through nuances in inflection appropriate for different languages.

Smallwood is interested in the ways that data science guides content and helps people “understand regions and cultures around the world through storytelling.” She enjoys the fact that her job allows her to engage and learn as well.“I, myself, have learned so many things from watching different pieces of content. You learn something that’s much more subliminal or that can really impact your empathy when you relate to a character and see the details of how they live their lives in an entirely different culture. And that’s different than reading a news article about a culture,” she says.

As to her own future, Smallwood expects to stay at Netflix for a long time. “There are just such massive, new, exciting problems that we’re working on now, and I can’t imagine that changing.”

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Janet George | The Multifaceted World of Data Storage

“Fail fast” has become something of a mantra in Silicon Valley. But Janet George, the chief data officer of data storage giant Western Digital, has an amendment to that conventional wisdom: “Fail privately.”She suggests that failing privately allows you to open yourself up to discovery and exploration in a safe setting where you are able to take risks. “Carve out time for yourself so you can fail privately. So, you take 20 percent of your time in big initiatives you feel you can really contribute to, but take 20 percent of your time [(where you can])fail privately.”

George, who has worked for some of the most important companies in the technology industry, shared this piece of advice, her career trajectory and the role of data science in the storage industry for the Women in Data Science podcast at Stanford University. Although the fear of failure is natural, it should never become a reason to avoid risk, she says. Taking an executive role at a storage company was a risk for George because she knew little about manufacturing before and. “I had to learn deeply about the device physics domain,.” she says. She became familiar with arcane matters like bit counts, failure rates, temperature testing and the impact of voltage on storage cells in order to ensure her success.

Now in her fourth year at Western Digital, George continues to notice how much data science comes into play across the spectrum of the company’s business. From manufacturing to security and product development, “every aspect of mathematics, especially linear algebra, plays a very significant role,” she says. “When you think about the computations of scale, when you think about genetic algorithms, its applications, regression-type algorithms, or you even think about neural networks, it’s computationally heavy, it’s mathematically heavy.” Creating a die, essentially a mold, for a new storage device, for example, starts with tens of thousands of possible parameters. Data scientists at the company have to sift through a multitude of mathematical possibilities and discover the 20 or 25 most critical parameters. As the only woman at most executive meetings, George is wielding influence as a lone voice at the table, a skill honed over many years with important risks taken along the way. Her advice for aspiring data scientists: Build relationships and credibility within your organization and lead by example.