Key skills
AWSPythonPyTorchScikit-LearnSQLTensorflowMachine LearningMLTensorFlowscikit-learnMLOpsAnalyticsGitVersion Control
About this role
Role Overview
- 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)
Requirements
- 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.
Tech Stack
- AWS
- Python
- PyTorch
- Scikit-Learn
- SQL
- Tensorflow
Benefits
- Limited Travel required