Senior Data Scientist - Real-Time Esports Predictions

Stack

Responsibilities

• ▹Lead model R&D: design, build, and optimise the ML models and statistical frameworks powering real-time odds and betting markets
• ▹Advanced feature engineering: extract deep predictive signals from raw, high-frequency esports telemetry
• ▹Build state-of-the-art models, focused on performance and probability calibration
• ▹Design rigorous backtesting frameworks to prevent data leakage and evaluate performance against historical market baselines
• ▹Develop market logic: create the mathematical rules and probabilistic derivations that translate win probabilities into derivative markets (handicaps, totals, player props)
• ▹Deploy real-time production systems: translate models into production-grade pipelines and microservices

Requirements

• ▹5+ years of professional experience in data science, quantitative research, or statistical modelling
• ▹Deep, intuitive understanding of probability, statistics, and machine learning theory
• ▹Advanced Python proficiency, expert-level skills in the Python data stack, production-grade code
• ▹Proven experience designing complex backtesting environments and defining custom evaluation metrics

Nice to have

• ▹Deep esports domain expertise (CS2, Dota 2, LoL), game mechanics, and the competitive meta
• ▹Experience modeling high-frequency/real-time, continuously updating data
• ▹Software engineering basics, MLOps principles, tools like MLFlow, Airflow

Soft skills

About the company

GRID builds real-time prediction systems for competitive esports (CS2, Dota 2, League of Legends). Their models power live betting markets, producing continuously updated win probabilities, handicap lines, over/under totals, and specialty markets during matches.