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

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
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.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data modelingevent-probability modelsathleticism modelsfeature extractiondata validationPythonRSQLstatistical analysisdeep learning

Soft Skills

attention to detailteamworkcommunicationproblem-solvingreliability