Data Scientist

Responsibilities

Design, build, evaluate, and maintain predictive statistical and machine learning models for player evaluation, projectable skill growth, player acquisition, tactical simulation, and performance optimization.

Extract actionable insights from spatio-temporal tracking data to drive quantitative player evaluation.

Conduct rigorous exploratory research using advanced quantitative techniques (e.g. Bayesian inference, spatial modeling, survival analysis, or deep learning) to uncover unexploited market inefficiencies.

Systematically audit, backtest, and refine existing internal predictive models to ensure high accuracy and adaptability through changes in game rules or industry economics.

Partner closely with data engineering teams to design scalable features, automated data pipelines, and production workflows for seamless model deployment.

Translate complex probabilistic outputs and model predictions into intuitive visualizations, executive briefs, and actionable insights for front-office leaders, coaches, and scouts.

Qualifications

Master’s or Ph.D. in Statistics, Data Science, Computer Science, Applied Mathematics, Operations Research, or equivalent practical quantitative research experience.

Deep statistical learning knowledge and hands-on experience applying machine learning techniques, such as gradient-boosted decision trees, hierarchical/mixed-effects models, neural networks, or spatio-temporal modeling.

Advanced programming proficiency in Python and/or R for numerical computing, data manipulation, and model development (using libraries such as scikit-learn, PyTorch, XGBoost, tidyverse, or PyMC/Stan).

Strong command of SQL for extracting, aggregating, and joining large-scale relational datasets.

Practical experience with software development best practices, including clean code principles, version control (Git), unit testing, and reproducible research workflows.

Nice-to-haves:

Experience with stochastic simulation methods (e.g., Monte Carlo) or reinforcement learning for game strategy optimization and decision modeling under uncertainty.

A portfolio of public sports analytics research, open-source sports data science projects, or competition entries evaluating athlete performance or tactical dynamics.

Familiarity with modern MLOps workflows, containerization (Docker), and cloud infrastructure (AWS or GCP) for scaling predictive models.