Responsibilities
Design, build and optimise the machine learning models and statistical frameworks that power our real-time eSports forecasting.
Extract deep predictive signals from raw, high-frequency data streams, starting with live eSports telemetry, turning complex in-game mechanics into structured modelling features.
Focus on model performance and probability calibration. Design rigorous backtesting frameworks to prevent data leakage and evaluate performance against historical forecasting benchmarks.
Build the probabilistic framework that turns live game-state and event-level predictions into consistent forecasts for game, round and match outcomes, score differences, aggregate game statistics and player performance.
Ensure your models are translated seamlessly into production-grade pipelines and microservices.
Requirements
At least 5 years of professional experience in data science, quantitative research or statistical modelling, including ownership of complex modelling work.
Strong applied knowledge of probability and statistics, demonstrated through probabilistic modelling, calibration or comparable quantitative work.
Strong Python skills and experience writing clean, maintainable research and production code.
Proven experience designing rigorous backtesting or temporal validation environments, preventing data leakage and defining evaluation metrics for unusual business problems.
Benefits
Working location: Remote full-time ([protected info] ICT)
Salary range: USD 4,000 - 8,000 gross
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