Particle Physics // Machine Learning // Music // Baseball
Over the last few months, I’ve applied for several jobs in MLB front offices. I wanted to recount my experience so others could see what this process looks like: the work required in the application process, the application timeline, people you interact with, and so on. For visitors, I’m a Ph.D. student working in particle physics writing my dissertation and am looking for the next step afterward.
Most of my research is data analysis on large datasets, and I do baseball analysis in my spare time, often with similar methodology to my research, so I figured applying for these jobs would be a logical progression of my career, and one where I have experience. I’m purposefully omitting specifics on questions I was asked in questionnaires and names as well, however, I hope this account is interesting nonetheless.
Admittedly, I applied here way before I should have started applying for roles, but I saw this listing go up last summer and was interested. I submitted an application through a site called TeamWork on June 23, and the next day I received notice that I passed through to the second round of their process. This was a timed questionnaire, with no hard cap on the time to take, but recommended to be 90 minutes or so. It took me around 2 hours, personally. It was about 4 pages of SAT-style questions, primarily based on pattern recognition. Finally, there was a page of 5 longer-form questions. 4 of which were stats-based, one with a closed-form solution and 3 I solved through simulating in python. The last was physics-based, a straightforward projectile motion problem.
I passed this round, and continued to the third round of their process, in which I was given a week-long data project, involving making a projection system based on inputs from 2 systems measuring the same parameters. This also incorporated “messy” data and how one would approach this. It was suggested to spend about 4 hours on this, I ended up spending close to three times that. On returning the project I also was asked when I expected to graduate as well (though listed on my resume, may have been overlooked). After this step, they elected to continue with other candidates.
I applied for the Senior Data Science role with the St. Louis Cardinals at the end of September. I sent in my resume and received a response about a week and a half later, continuing to the next round. They asked for responses to 5 questions, limiting each to 300 words or less. These questions were a mixture of assessing my data analysis and modeling background and my baseball knowledge. I was given a week to work on them and submitted them 4 days later, October 14.
On October 24th I was notified I made the next round of the process, a phone interview. This was a 1-hour call on November 1, with their Director of Analytics, Senior Director of Baseball Development, and their Project Director of Baseball Development. Much of this call I ended up talking about physics, the experiment I work on, the data challenges I’ve faced and so on. The next day I got a call from the person conducting the hiring process, who asked me to come in for an in-person interview, which I scheduled for the following week, November 8th.
I drove to St. Louis the night before and stayed with my parents who live in the area. The next morning my interview started around 10. The interview started by touching base with the person conducting the hiring process, the Baseball Development Project Director. It was a half-hour, followed by an hour and a half block with the Baseball Development department. This included the same people I spoke with on the phone interview. They put me in front of a whiteboard and talked with me about some baseball topics, asking me to draw how I might expect certain variables to look, or how I might model a distribution. There was also a few lines of whiteboard coding, explaining how I would make a plot in my language of choice.
After this, I met with some Assistant GMs, one primarily involved with international operations, and one who serves as a director of scouting. The latter gave me quite a hard time about wanting to pivot my career out of physics, and was surprised to find that many of my family and colleagues supported my interest in applying to baseball jobs. Afterward, I met with a pair of player development managers, and then a pair of baseball analysts. Both of these meetings were pretty calm and just more informal “get to know you” type interviews. Last, the day ended with another chat with their Director of Baseball Analytics. Interestingly, he also came from a physics background so this was a nice chat, to sort of understand his transition as well. Unfortunately, this was the week of the GM meetings, so both the President of Baseball Ops and GM were out.
On leaving, I was told I should hear back in a few days. I was called about a week and a half later, on November 18th with news that they went with another candidate.
I sent in an application for a Data Scientist position with the Cleveland Indians on October 31st, and I didn’t hear anything back for quite some time… sort of. Several weeks later I was browsing my spam folder and noticed that they sent a questionnaire, and it had a week deadline that had already passed. Fortunately, I wasn’t the only person this happened to - I received a follow up on November 17th that mentioned this was a widespread issue, and I could continue if I was interested. I completed it that day - 3 questions, focused on projects I’m interested in and experience I’ve gained working on past projects.
On the 22nd I received an email asking to move forward in the process with a phone interview, which I scheduled for later that day. The person I spoke to on this call had a cursory knowledge of the experiment I work on and we talked about data challenges I’ve worked on and models I’ve built, as well as some talk about my background and my interest in baseball. It ran a half-hour. Following this, on December 3rd I was asked for a second phone interview, which we scheduled for the following day. This was with 3 people in their baseball R&D team, I don’t remember all the details of what we spoke on for this one, but it wasn’t all that dissimilar from the previous phone interviews, mostly getting to know my background and experience.
On December 9th, they followed up by a second, longer questionnaire. This questionnaire had 9 questions split into 3 categories: Baseball Valuation, Math and Research, and Analytical Questions. In each category, I was told to select 2 to answer, for a total of 6 responses, and each should be “no more than a page.” I ended up scraping data and putting together code for 2 of these problems, both in the Baseball Valuation section. One of the Math and Research questions involved me reading an article and commenting on it. The remainder were more conceptual answers. Admittedly, I gave many of my ideas that I would have preferred to keep to myself for personal publication, but with this being the end of the hiring cycle for baseball, I figured there wasn’t much to lose on my end by doing so. I spent quite some time on finalizing these responses and submitted a 6-page response on December 12.
On December 15th I spoke with the person coordinating the job search, informing me they wanted to bring me in for an interview. Being so close to the holiday season, they wanted to bring me in that week, and elected to do a round-trip all on December 18th. That day, I woke up at 3:30 am, was on a flight out of Midway by 6:00 am, and arrived at Progressive Field around 9 am.
I was met by the person who coordinated the job search for 30 minutes, outlining the plans for the day and what to expect. Then I met with a couple of assistant GMs for a half-hour, and afterward was brought into a conference room to meet with the R&D group. There were 4 people in the room, one of which was another Assistant GM, and about halfway through another R&D member video-conferenced in. We discussed some ideas I spoke about on my questionnaire and worked through challenges and thoughts about how you might build the models. Through this, they asked quite a few high-level stats and data analysis questions. We went to lunch after this (I had a very tasty chicken and brie sandwich), and then continued time with the R&D department after lunch.
By this time, I was quite exhausted after having been up since 3:30 and having a full stomach, and if I had to isolate my weakest portion of the interview, this would be it. On the way back from lunch, I was discussing with one of the R&D members recent published work on uncertainty for neural networks, and when we got back he decided to ask more questions on uncertainty measurements, and I unfortunately spaced on bootstrapping methods when asked about data-driven uncertainty quantification. Once he mentioned this, I was back on track and ended on a decent note, but this was certainly where I looked the weakest.
The rest of the afternoon, I had three meetings, each about 45 minutes, with various members of their front office, including the Assistant Director of Baseball Operations, VP of Player Acquisitions, Assistant Director of International Scouting, Baseball Operations Assistant, Director of Learning and Development, and Assistant Director of Player Development. These were more traditional-style interviews, asking about my experience working with others, conflict resolution, how I can take my work and explain it to non-analytical people, and so on. All of these went well, and the people were all fantastic and fun to talk to. The day ended with another 30 minutes with the R&D team, asking some quick questions on basic linear models, and then sending me off. I was on a flight by 6 pm, landed by 6:10 (time zones are fun), and completely exhausted in bed by 9 pm.
At the end of the interview, the person coordinating it mentioned that it would be some time before I heard back, due to the holiday season, followed by him being on vacation. I sent a follow-up email the next day thanking for the experience and he mentioned that I should hear back mid-January. After a month, I sent an email on January 15th, just to touch base, and was told they would have an answer by the end of the following week. I received a call on January 23rd from the person coordinating the search and an Assistant GM informing me that they went with another candidate. As the cycle was over and I’ll be applying for more mainstream data science and academia roles, I asked if they had any feedback to better my interviewing in the future, and they said that they really enjoyed my interview and talking to me, didn’t have anything that stood out that I needed to improve on, but that the candidate they selected had more of a classical statistics background.
Unfortunately, I don’t have a job lined up just yet. I’ve got a few other irons in the fire - one in the sports sector, a few within academia, and a few other areas of data science. Neither of these interviews provided very substantive feedback in terms of what I could do better moving forward, so I’ve tried to do some reflecting on it. I took a look at the candidate the Cardinals ended up hiring, who has a decade of experience in industry doing modeling, so probably a portion is experience. My background being in physics, and our analyses are somewhat opaque, it might be hard to see how my experience overlaps into the baseball domain clearly. While I have some work on the community research portion of FanGraphs, I could do better in uploading some of my public models as I work on them, so my experience translates more transparently. Beyond that, it’s probably a good sign that I made it at least to an in-person interview with the teams I applied for, so at some rate, it’s a matter of time before something works out.
Otherwise, I wanted to close with some other broader takeaways:
These application processes absolutely monopolize time. All the questionnaires, phone calls, time interviewing, time traveling, and so on really add up. I will admit that I am likely more sensitive to this at this point since I’m simultaneously writing my dissertation and trying to finish my physics analysis as well.
MLB R&D departments are smaller than you might expect, at least in my sample size of 2. Both of the teams I interviewed with, it was just a handful of people. These people are very bright, along the way I met another physics Ph.D. and a statistics Ph.D., but from listening to a game broadcast, you get the impression these are huge teams of people, which is not the truth.
Almost everybody I encountered through these experiences were fantastic, a lot of fun to talk to, and passionate about the game. For both interviews, it was an incredible amount of fun to take a day away from work and get to talk to so many people who think about the game and to talk about baseball at a deep level.