Data Scientist II (Basketball/Hockey)

Teamworks

About The Position

I'm Tyrel Stokes, Senior Manager, Data Science at Teamworks. I lead the data scientists behind our Hockey and Basketball platforms, and right now we're hiring for both — one Data Scientist focused on Hockey, one on Basketball. These are teams I'd put up against anyone doing technical work in sports analytics today, working with tracking and pose data that most researchers and analysts won't have access to for years. Both roles are for people who want to get into the data, understand it deeply, and build things that last. The work is different by sport, but the profile is similar: strong data science fundamentals, an eye for how data should be structured to support modeling, and genuine passion for the sport and the analytics pushing it forward. The Role Both roles involve building on top of proprietary tracking and pose data to create metrics, models, and analyses that elite sports organizations actually use. Day-to-day the work looks like this across both: Build and transform new data sources into tables, features, and structures that are easy for the team and our clients to build on Develop, extend, and validate models — including event-probability models and athleticism models — ensuring data representation supports both current and future use cases Build metrics and analyses that NHL and NBA clients rely on to make decisions, and support client-facing work by digging into the data to answer their questions directly Extract meaningful features from high-dimensional tracking and pose data, and update existing models to incorporate new signals as they become available Validate models and outputs — your own and others' — with enough rigor that the team can trust what ships Write clear reports that communicate technical work to the product team and broader organization The Hockey role centers on expanding our platform to non-NHL contexts using Sportlogiq tracking data and incoming Hawk-Eye pose data — including stick location, body orientation, and skating models. The Basketball role centers on building metrics and player evaluations on top of our EPV (Expected Possession Value) framework, translating model outputs into tools NBA front offices can actually use. Why This Role Hockey and basketball analytics are both moving fast, and these teams are at the front of it — working directly with NHL and NBA organizations on decisions that matter, using data most researchers won't have access to for years. If you want to do work that pushes the field forward and have it actually land with real teams, these are rare seats to be in. About Teamworks We're the Operating System for Sports™, powering 6,500+ organizations worldwide, from collegiate programs to every major pro league. Founded in 2006, we've evolved from a messaging tool for college football into the leading sports tech platform, with 500+ global teammates building the future of sports tech. Our solutions span Personnel, Coaching, Performance, Operations, and Intelligence - helping teams recruit smarter, train better, stay compliant, and win. Teamworks is an equal opportunity employer - if you live our core values every day and are honest, hardworking, humble, committed, innovative, and an all-around exceptional person, you'll thrive at Teamworks. We are committed to building a diverse and inclusive workforce and take affirmative action to not discriminate based on race, religion, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, age, sexual orientation, veteran or military status, or any other legally protected characteristics. This policy applies to all employment practices within our organization, including but not limited to recruiting, hiring, promotion, termination, compensation, benefits, and training. Teamworks is committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process. To request a reasonable accommodation, please email [email protected]. To all recruitment agencies: Teamworks does not accept agency resumes. Please do not forward resumes to our jobs alias, Teamworks employees or any other organization location. Teamworks is not responsible for any fees related to unsolicited resumes.

Requirements

3+ years of experience working with sports tracking data including the kinds of models typically built and the data challenges that come with them
Strong data science fundamentals: you understand how models work, what they need from the data, and how to set data up to support them
Proficiency in Python or R, with solid statistical foundations and comfort with SQL for building and querying structured data
Attention to detail in how data is structured and represented, with an eye for edge cases, consistency, and how downstream users will interact with what you build
A team-first mentality — both teams are small, and being someone others can rely on matters as much as technical skill

Nice To Haves

For Hockey: direct experience with hockey data or hockey analytics (inside a team, public work, or academically), and familiarity with pose or skeleton data or other high-dimensional spatiotemporal data sources
For Basketball: experience working inside an NBA front office or comparable environment, familiarity with deep learning methods, and public-facing basketball analytics work that demonstrates how you think about the game

Responsibilities

Build and transform new data sources into tables, features, and structures that are easy for the team and our clients to build on
Develop, extend, and validate models — including event-probability models and athleticism models — ensuring data representation supports both current and future use cases
Build metrics and analyses that NHL and NBA clients rely on to make decisions, and support client-facing work by digging into the data to answer their questions directly
Extract meaningful features from high-dimensional tracking and pose data, and update existing models to incorporate new signals as they become available
Validate models and outputs — your own and others' — with enough rigor that the team can trust what ships
Write clear reports that communicate technical work to the product team and broader organization

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