Note: The job is a remote job and is open to candidates in USA. Teamworks is the Operating System for Sports™, powering over 6,500 organizations worldwide. They are seeking a Data Scientist II to build metrics, models, and analyses using proprietary tracking and pose data for elite sports organizations, focusing on both basketball and hockey analytics.

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

Skills

• 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

• Offers Equity
• Offers Bonus

Company Overview

Company H1B Sponsorship