Podcast Episode #3 – Neil Pierre-Louis – Boston Red Sox

In the third episode of the Sport Analytics Podcast, our host Amrit Vignesh sits down with Neil Pierre-Louis, a Software Engineer for Baseball Systems at the Boston Red Sox. Neil takes us on a fascinating journey—starting as an Environmental Science major, interning at Google, leading analytics projects for UNC Baseball, and now contributing to an MLB front office.

Neil explains how he built a robust foundation in SQL and software development, created hockey analytics tools like expected goals models, and collaborated with coaches to turn raw data into strategic insights. He also offers candid advice on building a standout portfolio, navigating cross-departmental projects, and knowing when (and how) to showcase your work. Whether you’re already in the sports analytics space or just exploring your passion for data-driven insights, you won’t want to miss this deep dive into the intersection of tech and baseball.

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Amrit (Host): Welcome to the third episode of the Sport Analytics Podcast. Today, we’re joined by Neil Pierre Louis, who is a software engineer for baseball systems at the Boston Red Sox. Neil, how are you doing?

Neil: Doing well.

Amrit (Host): Great to hear. Let’s jump right in. You started as a software engineering intern at Google before diving into college baseball analytics at UNC, then moved on to the Boston Red Sox. Could you walk us through these transitions and share how each experience shaped your career path?

Neil: Definitely. I’m currently a software developer for the Boston Red Sox in the baseball research and development department. My path wasn’t straightforward: I entered college as an environmental science major and never expected to be where I am now. I took an intro to computer science class at UNC, which really sparked my interest. My dad was in biostatistics, so I considered combining computer science with my love for sports.

I interned at Google twice—first on Google Flights, then on Android Messages. Those were great experiences in large-scale software development. During this time, I got into the sports analytics scene at UNC. I joined UNC Baseball Analytics, worked as an analyst, and eventually led projects. I found the Red Sox role, which combined my Google experience with baseball analytics, and I’ve been here for about a year and a half now.

Amrit (Host): You’ve also created some advanced hockey analytics tools, like your expected goals model. What were the biggest challenges in shifting between hockey and baseball analytics, and how did your previous experiences inform how you build sports-specific models?

Neil: My first big project was building a scraper for hockey data, which was tricky because I started with minimal Python and API knowledge. But it taught me a lot about data gathering and cleaning. When I worked with UNC Baseball, a lot of the analytics tasks were project-based, responding to requests from coaches or directors. That meant focusing on more defined goals rather than freeform exploration.

Amrit (Host): How have your responsibilities changed from the UNC role to your current position with the Red Sox, especially in terms of deadlines and project scope?

Neil: Working at UNC really prepared me for MLB’s seasonal deadlines. In college baseball, you have an offseason where you need to get a lot done, and once the season starts, there’s limited time for big projects. It’s similar in MLB—if a project for the draft isn’t finished before the draft, it’s useless. My UNC experience managing a small team taught me how to handle multiple tasks under tight deadlines, which is crucial in pro sports.

Amrit (Host): You’ve been exposed to a wide range of technologies, from Java and Angular at Google to Python and R in sports analytics. Which tools and languages do you rely on most day to day, and are there emerging technologies you’re excited about for sports data?

Neil: On my personal website, I use Angular for the front end, Node.js and Express for the back end, and a SQLite database. At the Red Sox, our stack involves C# for the back end, Angular for the front end, and SQL for our database. I can’t stress enough how important SQL is, especially as data volumes grow with biomechanical and tracking data. Efficient querying is essential when dealing with massive datasets in sports.

Amrit (Host): With experience at Google, UNC Baseball, and now an MLB front office, what advice would you give someone wanting to follow a similar path, particularly if they have multiple sports interests?

Neil: Know that working in sports can feel like a startup: fast-paced, tight deadlines, and lots of collaboration. At Google, I spent six weeks just writing a design document for my summer project. Here, I had an impact within a month. Each environment has pros and cons—larger companies have structured processes, while sports offers immediate, tangible results. Be prepared for a more “all-hands-on-deck” vibe in sports and make the most of the rapid learning opportunities.

Amrit (Host): From a hiring perspective, which portfolio projects or demonstrations best showcase a candidate’s potential for sports analytics or development roles?

Neil: Having a public portfolio is huge. For developers, a full-stack web application that aggregates or visualizes baseball data can really stand out. If you want to be an analyst, consider a personal blog or Medium articles with in-depth research. Posting on Twitter or GitHub is a great way to get feedback and be noticed. Passion projects that mirror what teams do internally can showcase both skill and enthusiasm.

Amrit (Host): You majored in computer science with a statistics and analytics minor at UNC. Which essential topics or skills should certification programs emphasize for those aiming for advanced analytics or baseball research and development?

Neil: SQL is critical, and knowing Python or R is standard for analysts. For developers, front-end frameworks like Angular or React and back-end fundamentals are important. Schools may cover much of this, but personal or open-source projects will refine those skills. Tailor your learning to the specific role you want—if you see a team using React, invest time in React.

Amrit (Host): How did collaborating with coaches and teammates at UNC prepare you for cross-departmental projects in an MLB front office?

Neil: Communication was vital at UNC. Coaches often don’t have deep statistical backgrounds, so presenting insights in an understandable way was crucial. Learning to translate complex data for non-technical stakeholders is a skill that directly applies in MLB. If a feature is too big a technical lift, you have to negotiate the best way to achieve their goals without blocking deadlines.

Amrit (Host): Whether modeling expected goals in hockey or pitch tendencies in baseball, do you see overarching analytical principles that apply across sports?

Neil: Absolutely. Every sport involves data cleaning, efficient querying, and a strategy for handling large datasets. Baseball’s discrete events simplify isolating player performance, while hockey’s continuous play makes it tougher to parse. But good data practices and a methodical approach to analysis apply to both.

Amrit (Host): Baseball analytics is rapidly evolving with new data like Hawkeye and advanced ML approaches. Where do you see the next wave of innovation?

Neil: Biomechanical data is enormous. Teams can track physical movements more precisely than ever, which could lead to breakthroughs in injury prevention and performance optimization. This tech will likely transform many sports as teams figure out how to apply it in real time.

Amrit (Host): Do you have any final advice for aspiring software developers or analysts, and any thoughts for us as we grow this podcast and educational platform?

Neil: First, keep doing what you’re doing—platforms like this provide valuable insight for newcomers. Second, build that portfolio. Each new project, even if it feels amateur at first, will improve your skills and visibility. Third, if you’re still in school, make sure to enjoy that time while also sharpening your technical abilities. Balancing intense workloads is great training for a sports analytics career.

Amrit (Host): Thanks so much, Neil, for sharing your journey. It’s been really enlightening to hear about your experiences.

Neil: Anytime. Thanks for having me.