Landing a Job in Baseball Analytics with Neil Pierre-Louis!

Meet Neil Pierre-Louis.

Neil has always been a sports guy, but he didn’t always have aspirations of working in the space. He started his college journey in 2019 at the University of North Carolina Chapel Hill as an Environmental Science major. That lasted for roughly 2-weeks, not because he had his epiphany about working in sports, but because COMP 110 (Intro to Computer Science) had hooked him on the world of computer science.

After switching his major, Neil’s freshmen year at UNC took a detour when the COVID-19 pandemic hit, sending him home for the rest of the school year and the ensuing summer. Disappointing (any of us who were in college at the time can certainly relate), but two critical developments occurred during this time. First, conversations with his dad about quantitative career paths led to Neil adding a minor in statistics. And second, and more importantly, the time at home gave Neil the opportunity to learn coding beyond what had been introduced to him in class.

One of Neil’s most important lessons for computer science/data analytics majors is to understand that class alone isn’t going to provide you with adequate coding skills to succeed in the industry. The majority of his skill set was developed through pursuing side projects. For Neil, this started during Covid when he started following hockey analytics creators like JFreshHockey on Twitter, sparking his curiosity for applying his coding and statistics background to sports.

His first personal project was to create a Discord bot that notified users when NHL games went to overtime, prompting them to vote on who they thought would win, and then tracking and measuring results automatically for each participant. Jumping ahead to his junior year, his next big project was to build an expected goals machine learning model that predicted the likelihood of a goal based on relevant shot data. By his senior year, Neil had built and launched his own website to showcase his various models and visualizations within hockey (check it out here). These projects prepped him for his internship opportunities throughout college and was pivotal to landing his role with the Red Sox, serving as a portfolio piece that demonstrated his technical abilities.

Back to the story. Neil’s first official internship was a big one - Software Engineering Intern for the Flights Team at Google (yes, that Google). He applied and interviewed for the position in October of his sophomore year through Googles STEP (Student Training in Engineering) program (learn more here), and by November had received his offer for the ensuing summer. How do you land at Google for your first internship? Be really ******* good at coding.

Neil trained and prepared for his interviews using LeetCode, a platform “to help you enhance your skills, expand your knowledge, and prepare for technical interviews.” Their words. Neil described it as every software engineer’s worst nightmare. Neil spent hours answering practice questions on the platform, crushed his technical interviews, and closed the deal. Neil’s time at Google was a success, so much so that he received a return offer for the following summer to support the Android Messages team. Big win.

After his first summer with Google, Neil returned to UNC for his junior year of school and got involved with two different opportunities. The first was with SickleSoft Inc, a MedTech startup focused on reducing hospital readmissions through improved pain-reporting systems. Neil worked across software development and data visualization, wearing many hats in the startup environment.

Neil eventually left SickleSoft for his next opportunity, which would launch his career in sports. Neil was recruited by a friend to be a student analyst for the UNC Baseball program. Neil was one of ten student analysts and had responsibilities including video collection, running the TrackMan, and providing pre-game scouting and statistical reporting. A dream role for a college student. In addition to the cool factor, the experience also taught him to translate technical analysis into actionable insights that coaches, and anyone else not technically trained in data analytics, could understand and apply.

Neil was a standout on the team and was promoted to project lead for his senior year, taking on a larger technical role and leading a team of analysts. One of his more impactful projects was leading the development of a pitch-prediction model for the coaching staff. Cool stuff.

At its core, data analytics in sports is about turning information into insight. Every pitch, swing, and player movement generates data, and teams rely on analysts and developers to help interpret what it all means.

Data professionals like Neil build the systems and models that power decision-making across every level of an organization. From designing internal tools used by coaches and scouts to visualizing data for player development and strategy, analytics is the engine behind modern performance and operations.

For professionals, this field blends technology, statistics, and problem-solving. Whether your background is in computer science, math, or even something entirely different, analytics offers a path to impact the game from behind the scenes

With a second internship at Google and his new role with the baseball team under his belt, Neil began his post-grad job search during his final year of college. He originally intended to return to Google, but when his offer fell through amid tech hiring freezes, he turned his focus to MLB front-office data roles. One of such roles was with the Boston Red Sox. Neil went through multiple rounds of interviews including a day in Boston that featured seven different conversations and ultimately received his offer right after finishing his last final. Cinematic.

Neil has now been a developer, baseball systems, for the Boston Red Sox for 2.5 years. He is responsible for building internal applications, tools, and visualizations that are used by coaches, scouts, and executives for data-driven decision making. His specific focus is on front end representation and the user experience, ensuring that data is digestible and actionable for the baseball personnel, just like he was doing at UNC.

His team consists of 40+ staff members (5 developers, 10-11 engineers, 20 analysts), making it one of the MLB’s largest data analytics groups.

Q. What are the most important coding languages to learn for those who want to work in data analytics or software engineering? How can those new to coding get started?

A. I'd say the most important for analytics really depends on which sport you want to get into, but regardless, SQL should be up there. Ideally you are at a team where you are not doing any crazily complicated queries, but knowing good database practices will set you up nicely. Back to my original point, as an analyst you should definitely know R or Python. I think most of the baseball industry skews towards R, but I know some teams are python based. I definitely have a preference (Python lol) but get familiar with building models in both. For software engineers, I would not say a specific language (if I had to, JavaScript) but more so just understanding how full-stack applications work and trying to get your hands on building one.

As far as best ways to get started: we are truly spoiled to be living in this age. There are so many resources out there, almost too many, so I can share some of the ones that helped me.

Introduction to Statistical Learning: It's always good to have a strong foundation of the basics. This was basically the textbook of my machine learning class and it was helpful. Linked the entire textbook below, great way to learn some python. Any YouTube/coursera course will also be good for that.

https://hastie.su.domains/ISLP/ISLP_website.pdf.download.html

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems: Maybe the most useful resource I've ever bought lol. Great for having concise explanations and walks you through several model building sessions/techniques. GitHub as well as a book.

I also have written an article on building your own full-stack web app for sports. https://medium.com/@neilpierre24/a-guide-to-building-your-own-full-stack-sports-analytics-website-a9247cb3b99f

A. Having domain knowledge is huge and it further reinforces your statistical/data visualization skills, whether it's knowing what variables could be important for a certain output or how to display analysis in a certain way.

A. I'd say the biggest difference has been my output just due to the sheer size of Google. For good reason, there are a lot of restrictions, things move slowly and need lots of approval. With the Red Sox though, competing with other teams mixed with hard deadlines and a much smaller team results in a similar environment to a startup. We ship a lot of code pretty quickly (sometimes I do it too quickly lol), which has been great for me, I've learned a lot in a short amount of time and have also made an impact. 

A. I think it's something that you ultimately learn by experience, something that helped me was walking myself through a project I had done for my Twitter or for school and trying to envision understanding it from a perspective that had not seen it before. Biggest thing I've learned so far being here is just how to manage my time and resources properly, which isn't a concept I would have thought to be super important, even after my internships. But with how many different moving parts there are to a baseball operation, it's really important to dedicate the correct amount of resources to the different work that you have.

1. It’s never too late to pivot.
Neil didn’t start his career in sports - his first internships were at Google. But the skills he built there translated directly into sports analytics. It’s proof that even if your path starts outside the industry, you can always bring those experiences back in.

2. Learn by doing.
Classes built the foundation, but side projects built the required skillset. Every project, from his first Discord bot to his hockey website. taught Neil something new about problem-solving, data, and creativity, preparing him for his position with the Red Sox.

3. Communication matters as much as code.
In baseball analytics, data only matters if coaches can use it. Learning to translate complex models into simple insights became one of Neil’s most valuable skills.

Google Programs for Students

Boston Red Sox Job Board - (20+) jobs available

A heartfelt thank you for reading through this edition of So You Want to Work in Sports… It means the world to me.

If you have any feedback, a guest recommendation, would like to be featured yourself, or have any questions, please email me at [email protected].