Ilya Vedrashko
Managing Director, Applied Data Science
Syneos Health
A People-Centric Scientist
Ilya Vedrashko, like most researchers, eagerly dives down rabbit holes in pursuit of answers. But what sets him apart is his ability to emerge from the warren clutching an answer that perhaps no one else could have found. Ilya heads Syneos Health’s newly formed applied data science team in the commercial strategy group. These scientists help life sciences companies optimize their omnichannel marketing approaches. Their approach, however, is a special brand of data science: Humanistic data science—seeing the people behind the numbers.
Ilya and his colleagues focus on understanding the social, psychological, economic, or personal phenomena behind the data, using an advanced toolset to extract insights that aren’t obvious. To do this, Ilya has assembled an eclectic team of PhD-level data scientists who represent a wide swath of human knowledge.
Ilya himself studied humanities at MIT, which may be why he can apply principles and models from microeconomics, linguistics, motivational psychology, evolutionary psychology, and even physics to data science projects. Ilya explains, “If we want answers that are different from everyone else’s we have to ask questions that no one else is asking. That’s natural for us because of our diverse backgrounds…we have to improvise and borrow mental models from our past experiences.”
In the past decade, Ilya has explored a number of breakthrough, and seemingly strange, research topics: How memes spread among people, how using a cell phone during TV ads affects recall of the ad, and what English speakers would choose to watch on Netflix if it were entirely in Chinese.
“Data scientists, of course, work with data, but what makes us different is our relentless focus on people,” says Ilya. “Our humanistic approach enhances the algorithms of data science with a thorough understanding of the social, psychological, economical, and personal factors behind the numbers.”
Ilya’s ability to see the possibilities in even the most mundane data sources and to experiment with the most novel methodologies is exactly why he emerges from the rabbit hole with an answer that has immediate value.
