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Strata speaker cites key characteristics for success in data science

Senior research scientist Rachel Schutt offers a rundown of the common traits shared by people who have been successful in the field of data science.

Strata ConferenceImage: Jun Seita

SANTA CLARA, Calif. -- Rachel Schutt understands what it takes to achieve success in the budding world of data science.

A senior research scientist at analytics startup Johnson Research Labs in New York, Schutt previously worked as a senior statistician at Google Inc. She also serves as an adjunct assistant professor in the statistics department at Columbia University, where she taught a course called Introduction to Data Science during the fall 2012 semester.

Speaking at the O'Reilly Strata Conference here this week, Schutt said she frequently brought in guest lecturers to talk about their careers in data science as part of an effort to provide her students with a mix of views and opinions. The list of guest lecturers included Cathy O'Neil, now a senior data scientist at Johnson Research Labs; David Huffaker, a user experience researcher at Google; and Ian Wong, an "inference scientist" at mobile payments processor Square Inc.

Later, Schutt compiled two lists in an effort to zero in on some of the characteristics she thinks are shared by successful data scientists. She presented those lists as part of her talk at Strata. The first one is a rundown of the common credentials and traits of the data scientists she considered. What she found is that many have doctorates in philosophy, although that degree isn't a requirement for the job, and that their fields of study were often in quantitative subjects, such as statistics or math. In addition, she said, they have an innate ability to code and learn programming languages, and they have proven problem-solving skills. Ironically, she noted, one thing they don't necessarily have is the phrase "data scientist" in their job titles.

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Schutt's second list describes what she called the common "habits of mind" of effective data scientists. For example, she said, they tend to be very persistent people who don't like to give up when faced with challenges. They also are flexible thinkers and are prone to asking questions. And they're the type of people who strive for accuracy, clarity and precision in their thinking and how they communicate, she added.

Successful data scientists also are adept at applying past knowledge to new situations, Schutt said. They take calculated risks in their analytics work, they're imaginative and they like to innovate. In addition, they think independently and believe in continuous learning. But they have a lighter side, too: She said they also tend to find the humor in things and to be good listeners who are empathic to the needs of others.

Schutt went on to stress how important those habits of mind are for aspiring data scientists. The ones who succeed "are thinkers, and they're curious and they're ethical and they have integrity," she said. "These are the sorts of things I also wanted to teach my class, beyond a set of skills."

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