An Interview with Barry Scharfman

An interview with Barry Scharfman, PhD, Head of Data Science at Sage Analysis Group (, an MIT Sloan spin-off with offices in Cambridge, MA and Arlington, VA. He completed his PhD in Mechanical Engineering at MIT in 2015 and was an active member of the Consulting Club at MIT. We ask him about his decision to become a consultant and the interview process.

Q) What post-PhD career options are available and how did you decide on consulting?

I was always interested in consulting as a possible career path as a way to combine my analytical skills developed during my engineering programs with strategic and business insights gained during my undergraduate business degree and extracurricular activities. While I did consider a career as an engineer, I was intrigued by the possibilities in consulting to have a greater impact on the bottom line or operations of large organizations and systems in a shorter period of time.

Q) How do you combine data science and consulting skills in your role?

My role is a hybrid of data scientist and strategy consultant. Most of our team members have strong technical backgrounds and spend at least part of each day developing models, writing code, and processing, analyzing, and visualizing data. Equally important is the ability to connect our analyses to overarching client needs and goals and to present relevant results clearly and effectively.

Q) What skills did you develop as a PhD that were applicable to consulting?

The capabilities to define and scope a problem based on limited information; ask the right questions and gather appropriate data; and quickly make progress toward a viable solution are critical as a PhD researcher and when starting a new project or task as a consultant. I also continue to use the data science skills that I developed during my Masters and PhD research in my work each day as a consultant to combine data sets from disparate sources, perform analysis, and visualize and build tools to deliver insights to clients.

Q) What aspects of consulting did you find were more difficult to adapt to?

When transitioning from the role of PhD researcher to consultant, a key difference is that in academic research, the goal is to make a new contribution in a particular field of study, while in consulting the focus is on delivering solutions and insights that meet specific client needs. While completely new analytical and strategic approaches may be necessary, the key success metric is how well the solution solves a client’s challenges. Even existing solutions must be adapted or tailored for each client project in order to be successful. Many PhD candidates lack some of the practical relevant skills needed for data science consulting. On the data science front, I recommend that students focus on learning widely-used industry standard tools and software languages (e.g. Python, R, and Tableau) as early as possible. Incorporating these tools into your research, project, or internship work is a great way to demonstrate your skills in these areas. It is also useful to learn about and/or become certified in other relevant areas, such as Agile project management, DevOps, and Design Thinking.

Q) How did you start your interview preparation, and how did you practice for case interviews?

I started preparing for interviews during the final year of my PhD program. I practiced for both fit interviews and cases with other PhD students from MIT and other universities in the area who I either met through my personal network or during the several consulting case competitions in which I competed. I practiced using case materials from MIT and other sources, read several consulting interview guide books including Case in Point by Marc Cosentino, and went through the Fast Math course and GMAT math problems to practice consulting math skills. I also attended on-campus information sessions hosted by consulting firms.

Q) From an interviewer’s perspective, what aspects about the candidate do you look for during the case interview?

Overall sharpness, organized thinking and structuring, facility with calculations and data, and creativity.

Q) How did you practice fit interview questions?

I built a matrix in Excel of my prior experience and stories that I would use to answer various standard fit questions. I also reviewed my resume quickly before each interview since I knew that interviewers might be looking through my resume as they asked fit questions. My colleagues with whom I practiced case interviews and I would quiz each other on fit questions. It was important to recall events in great detail and be able to drill in deep and answer any follow-up questions from the interviewer on the rationale for my actions. I used the STAR method and its variations.

Q) Can you talk about the interview process specifically for Sage Analysis Group?

For campus recruiting, we usually have a round of interviews on campus and another round at our offices (or virtually if necessary). Our interviews are a combination of fit and case questions. We sometimes also give take-home exams or cases.

Q) From an interviewer’s perspective, what aspects about the candidate do you look for during the fit interview?

I look for a record of strong achievement throughout the candidate’s academic and professional career. Leadership and being a good team player are also important dimensions of a candidate.

Q) If you had other options, what were they and why did you choose to join Sage Analysis Group specifically?

I switched from my prior consulting position at another firm to join Sage Analysis Group for an opportunity that focused more on strategy and advanced analytics. At Sage, I have worked on both commercial and government projects, while in my former role I focused on government projects.

Q) What makes Sage stand out from other consulting firms?

Sage provides strategic support to leaders in government and industry. One key differentiator is our team’s strong data science expertise combined with strategy consulting skills that the team uses to tackle every project. I enjoy being able to leverage my deep analytical background and engineering familiarity to solve clients’ strategic challenges. Unlike at many other consulting firms, at Sage I am able to keep my coding and advanced statistics and modeling skills sharp while applying them to new areas. Finally, the team itself stands out. This very sharp (many alumni from MIT and other top universities at all levels), close-knit community focuses on mentorship and staff development and growth while having fun tackling exciting challenges each day.

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