Data Scientist II (Basketball/Hockey)

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Company stage

Growth-stage From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Data Scientist II (Basketball/Hockey) United States, Canada 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

Read the full description at jobs.ashbyhq.com. FewerJobs shows a preview and links to the original posting.