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Sports Data Scientist

Anton Volkov

Full-time · Mid-level · New_York

About the role

We work with professional sports organisations — currently eight clubs across the NBA and MLS — to turn tracking data, event data, and video analysis into decisions: player recruitment rankings, injury risk assessments, in-game tactical adjustments, and season performance projections. The data we work with is genuinely interesting: sub-second spatial tracking from Second Spectrum systems, event-by-event match logs, biometric monitoring data from training sessions, and historical contract and performance archives going back fifteen years. The modelling is interesting too — survival analysis for injury modelling, graph-based approaches for passing pattern analysis, Bayesian hierarchical models for projecting performance under uncertainty. We're looking for a data scientist who has thought carefully about the specific challenges of small-sample, high-variance sports data: where you have 38 games in a season, not 38,000 observations, and where the signal is real but easily confounded by team effects, opposition strength, and a dozen other factors that naive models miss. Strong statistical reasoning, not just ML tooling, is the priority.

Responsibilities

  • Build and validate player performance projection models for recruitment analysis with appropriate uncertainty quantification
  • Develop injury risk models using biometric and training load data with clinically-meaningful output formats
  • Analyse passing and positioning data for tactical insights using spatial and graph-based approaches
  • Present model outputs, uncertainty ranges, and recommendations to club coaching and management staff in accessible formats
  • Maintain and document the modelling pipeline and evaluation benchmarks used across all club engagements

Requirements

  • 3–5 years of data science experience — sports analytics preferred, but deep statistical rigour in any high-variance domain is valued
  • Python — Pandas, NumPy, Scikit-learn, and at least one deep learning framework
  • Strong applied statistics: Bayesian inference, survival analysis, mixed effects models — you've implemented and interpreted these, not just referenced them
  • SQL for extracting and preparing large structured datasets
  • Strong data visualisation skills for presenting findings to non-technical coaching and management staff
  • Experience with spatial or graph-based data analysis is a meaningful plus
  • R fluency is useful given the breadth of statistical modelling we cover

Benefits

  • Serious statistical modelling work in a domain most data scientists find genuinely exciting — and with access to data most cannot get near
  • Full remote with occasional travel to client club facilities (4–6 times per year)
  • $90,000 – $112,000 base salary + performance bonus
  • $1,500 annual conference budget
  • Access to some of the richest sports data sets available outside top-tier clubs' internal research teams

Job Type

Full-time

Level

Mid-level

Language

English

Salary Range

$90,000 – $112,000

AI Expertise

data science & analytics

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