About the role
(100% remote, anywhere in Europe)
Are you excited about building ML systems that make predictions in real-time? Are you driven by building things end-to-end, from research to live systems?
At GRID, we are building real-time prediction systems for competitive esports (CS2, Dota 2, League of Legends). Our models power live betting markets, producing continuously updated win probabilities, handicap lines, over/under totals, and specialty markets during matches.
We are looking for a Senior Data Scientist to lead the research, design, and continuous improvement of our core predictive models. You will be the driving force behind the math, statistical logic, and feature engineering that make our models highly accurate and profitable. You will tackle complex problems in high-frequency data, design rigorous backtesting frameworks, and work on bridging theoretical research and live product features.
What you will do
Lead Model R&D: Design, build, and optimise the machine learning models and statistical frameworks that power our real-time odds and betting markets.
Advanced Feature Engineering: Extract deep predictive signals from raw, high-frequency esports telemetry, turning complex in-game mechanics into structured modelling features.
Build state-of-the-art models: Focus on model 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 baseline win probabilities into complex derivative markets (handicaps, totals, player props).
Deploy real-time production systems: Ensure your models are seamlessly translated into production-grade pipelines and microservices.
Your skills will include
Required
Experience: 5+ years of professional experience in data science, quantitative research, or statistical modelling.
Advanced Mathematical Foundations: Deep, intuitive understanding of probability, statistics, and machine learning theory.
Advanced Python Proficiency: Expert-level skills in the Python data stack. You write clean, production-grade code.
Evaluation Expertise: Proven experience designing complex backtesting environments and defining custom evaluation metrics for unique business problems.
Nice to Have
Esports Domain Expertise: Deep knowledge of competitive esports (CS2, Dota 2, LoL), the underlying game mechanics, and the competitive meta.
High-Frequency/Real-Time Data: Experience modeling off streaming data or data that updates continuously over time.
Software Engineering Basics: Understanding of modern MLOps principles and experience with tools like MLFlow, Airflow, etc.
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