AI Athlete Performance Trajectory Modeler

Trivon Data

Remote · $80k – $100k / year · Contract · Posted 4 months ago

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About the role

When a 24-year-old NBA player posts his best statistical season, is he entering his prime or having a career year? When a pitcher's velocity drops 2mph at age 30, does it stabilize or mark the beginning of a decline? Performance trajectory modeling — building data-driven estimates of where an athlete's performance is headed, not just where it is — is one of the most analytically interesting problems in sports. This is a 10-month contract to build the performance trajectory modeling system for our sports analytics platform, which serves professional team front offices and fantasy sports operators. You'll work with 15 years of player performance data across MLB, NBA, and NFL to build age-adjusted performance curves, peak-age estimation models, and trajectory forecasting that accounts for position, playing style, and historical comparables. The senior data scientist who leads our research team will set methodological direction. You'll implement the trajectory models, build the validation framework comparing trajectory forecasts against realized career outcomes, and create the player comparison interface that surfaces historically similar players with known trajectories.

Responsibilities

Build age-adjusted performance trajectory models for MLB, NBA, and NFL player datasets
Implement peak-age estimation using position-stratified historical performance curves
Design comparable player matching identifying historical athletes with similar trajectory patterns
Develop trajectory forecast uncertainty quantification capturing variance in projection ranges
Build validation framework comparing 3-year trajectory forecasts against realized outcomes
Create performance trajectory API serving projections to fantasy platform and front office clients

Requirements

3+ years in sports analytics, statistical modeling, or applied data science
Strong Python and statistical modeling skills with survival analysis or longitudinal data familiarity
Understanding of professional sports performance metrics and position-specific aging patterns
Experience with hierarchical or mixed-effects models for individual-level trajectory estimation
Interest in sports and ability to engage with front office clients on interpretation