Develop, validate, and deploy ML models for performance and operational use cases (e.g., predictive analytics, decision support, performance measurement)
Build data pipelines and analysis workflows for structured and time-series data
Implement monitoring and iteration practices for deployed models (MLOps basics)
Collaborate with engineering and performance stakeholders to translate requirements into deliverables
Contribute to ML infrastructure and codebase quality (reviews, documentation, reusable components)
Travel occasionally for live validation and stakeholder feedback (role dependent; approx. 5–6 race weekends/year for some assignments)

2+ years building production ML systems
MSc in Machine Learning, Data Science, Computer Science, or related field (or equivalent experience)
Strong Python and experience with ML libraries (scikit-learn and/or PyTorch/TensorFlow)
Experience with data handling and querying (SQL)
Understanding of model evaluation, deployment concepts, and version control (Git)
Ability to work in complex engineering environments and communicate with non-ML stakeholders
Advantageous would be: time-series forecasting, optimization, real-time systems, dashboards, sports/motorsport analytics, AWS experience.

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