Matt Weber is the Deputy Chief Data Officer for the Office of Justice Data, a unit within the New Jersey Office of the Attorney General tasked with supporting data analytics and transparency for the agency. His work for the unit to date has included several public-facing transparency projects, including recurring data releases for law enforcement demographics, discipline, and internal affairs, as well as staffing, strategy, and mentorship. Previously, he was a data scientist and team leader at Bloomberg LP, a software engineer at Big Health Ltd, and a postdoctoral researcher in the Center for Cognitive Neuroscience at Penn. He earned his Ph.D. in psychology and neuroscience from Princeton in 2009. He lives in West Windsor under a pile of semi-feral children. 

"My work entails about an even mix, these days, of statistics/machine learning and software engineering; I’ve also done a lot of internal education, mentoring, organizing and hiring.

Some academics are prone to believe that business problems are uninteresting. False! In my experience, at least, having a concrete context for a problem and a requirement to actually solve it subject to constraints, rather than just “shed light on it,” make life plenty interesting. And I never quit learning. When I left academia I didn’t know anything about modern neural networks, natural language processing, databases or any number of software engineering best practices.

Soapbox time: Academics have more skills than they think, if they can just find the words to explain them to hiring managers. Grad students are required to write and speak in ways that junior contributors in the corporate world don’t get to. Writing a co-authored paper is “project management.” Lab meetings are “using data to guide stakeholders on business decisions.” For most Ph.D.s, this stuff is going to work a lot harder for you than your deep subject matter expertise, so find ways to notice and foreground it.

On the flip side, some hiring managers believe that Ph.D.s (a) want to teach, (b) don’t want to take direction and (c) don’t want to work on anything that isn’t cutting-edge research. It’s not fair, but it’s what you’re dealing with. Tweak how you show up — on your resume, cover letter, interview answers, etc. — to mitigate those concerns.

And when you do succeed at your transition to industry, please keep your alumni networks in mind. I’ve spoken with a lot of college and graduate students looking into careers in data science, and my strong impression is that women and students of color are looking for career guidance at much higher rates than their representation in the student body. Take a few minutes to keep your information up to date so you can help your fellow Tigers out. Or send an email to GradFUTURES at [email protected] in the grad school — they’d love to hear from you!"

For the full story from which this interview is taken, please see this story by Denise Valenti and Liz Fuller-Wright.