Consulting to Data Science

At Interview Query, we love talking to our success stories.

This week I talked to an Interview Query member who recently joined Facebook as a data scientist. I asked him about his interview experience and how he transitioned from consulting to data science.

Check it out for insights into Facebook’s applied data interview, what data science skills you need to develop coming from a consulting background, and helpful interview prep tips.

For more tips on breaking into data science, see our guide: How to Become a Data Scientist in 2022.

I graduated with a degree in business with a concentration in statistics, so I was looking for something that aligned with that. I ended up at Applied Predictive Technologies or APT, which was acquired by Mastercard Data & Services before I joined but more recently integrated into the business. I felt that the company aligned well with my interests.

APT’s Test and Learn platform operates on a SaaS model, and we have teams that run engagements with large retailers, restaurants, etc. For the last couple years, I worked as a consultant with APT/Mastercard D&S.

It was more of a consulting role, as I was helping businesses run experiments in the Test and Learn software. One of the benefits of working in the consulting arm for the SaaS part of the business is that you do a lot of work in SQL, so I think having a bit of technical data analytics experience was helpful in getting more into data science.

Projects ranged from delivery of the Test and Learn SaaS platform to more traditional consulting engagements. Depending on your interests, it was easy to focus on engagements in business experimentation and analytics, which is where I spent most of my time.

One of the big reasons I wanted to transition to data science was that I wanted to better develop my technical skills. I loved working at APT, and am excited for the opportunity to get deeper into data science and analytics.

When I first started interviewing with Facebook, I’d learned to expect a mix of technical questions and business cases. Coming from a consulting background, I’d prepared for consulting cases in college, and generally knew what the process for preparation would look like. I read Case In Point and after that, it was mock interviews.

But when I learned about the style and topics of tech business case interviews, I was like, “I never thought about this before.” Especially for questions like X metric is down 5%, and having to think about the reasons for that. Considering pieces like what type of device saw the drop, or whether there was an error in the data collection, just wasn’t something I dealt with in my job day-to-day, so I knew that I had to figure out how to study and gain an intuition for these types of questions.

I was looking at more product management resources to start, which kind of worked. Those sorts of resources introduce you to how to think about these problems. So I was doing PM Exercises and Exponent, but I got the sense after the first round that they were looking for something a bit more tactical than the high-level you get with PM resources.

I feel lucky to have stumbled on Interview Query. One thing that really helped was the sample case questions. It’s the same flavor of questions from the PM exercises, but it gave me insights into how to structure an answer when you’re being interviewed by a data scientist.

The second piece was understanding how to answer different types of business questions. In consulting, it’s very easy to figure out what kind of business case you’re solving, but I found that the DS interview was more conversational and free-form. So I was trying to figure out: What makes this different? What should I focus on? Something clicked when I took the modeling course within Interview Query. I felt that it was super close to the structure that I was trying to figure out.

I would say reach out to people you know who are in the roles you’re interested in. For the people that have asked me what to do, I’ve been directing them to Interview Query.

But I think one of the biggest ones is just getting used to the types of questions you’ll be asked and the problems you’ll face. The questions you face in the product world or the tech world are a bit different. Through preparing for interviews, I think I’ve really been able to build up that product sense.

Looking for more insights into Facebook’s interview process. Check out our Facebook Data Science Interview guide, and our interviews with other successful candidates.