[Hiring] Data Scientist II @Teamworks

Role Description

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.

Qualifications

• 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.

Requirements

• 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

• Opportunity to work with elite sports organizations on impactful decisions.
• Engagement in cutting-edge analytics in hockey and basketball.
• Collaborative and innovative work environment.