Staff Data Scientist - GM Motorsports

• Define and evolve the data science roadmap for GM Motorsports in alignment with program and competition priorities
• Architect end-to-end ML and analytical solutions (from data ingestion and feature engineering to model deployment and performance monitoring)
• Establish and enforce best practices in experimental design, model development, validation, and MLOps within the team
• Lead high-visibility projects (e.g., race strategy models, performance prediction, simulation, telemetry analytics, audio/vision-based models) from problem framing through delivery
• Translate ambiguous racing and business questions into clear problem statements, analytical plans, and measurable success criteria
• Own technical decision-making, including method selection, trade-offs, and risk mitigation
• Partner with race engineers, competition leaders, and program managers to identify the most valuable AI/ML opportunities
• Build models and tools that directly support race weekend decision-making, vehicle development, and long-term performance analysis
• Ensure solutions are usable in real workflows: robust, interpretable where needed, and integrated into existing tools and systems
• Provide technical mentorship and code/analysis review for junior and senior data scientists
• Raise the bar on engineering rigor (testing, reproducibility, documentation, model monitoring)
• Help define career paths, skill expectations, and standards for the data science discipline within GM Motorsports
• Communicate complex analytical concepts and model behavior to non-technical stakeholders in clear, actionable terms
• Work cross-functionally with data engineering, software engineering, IT, and program leadership to deliver integrated solutions
• Contribute to a culture of experimentation, learning, and evidence-based decisions
• Champion data quality, governance, and responsible AI practices in model development and deployment
• Contribute to and help maintain shared libraries, templates, and tooling that accelerate future projects
• Support documentation and knowledge-sharing across teams and programs

• Bachelor's degree in Computer Science, Data Science, Statistics, Electrical/Mechanical Engineering, Applied Mathematics, or a related quantitative field
• equivalent experience considered
• Typically 7+ years of hands-on experience in data science / machine learning roles, with significant experience leading projects or initiatives
• Demonstrated experience in complex, high-stakes domains (e.g., motorsports, automotive, aerospace, manufacturing, or similar)
• Expert-level proficiency in Python and core data/ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch and/or TensorFlow)
• Strong foundation in statistical modeling, machine learning, and experimental design
• Experience building and deploying production ML systems (CI/CD for ML, model serving, monitoring)
• Proficiency with SQL and working with large, complex datasets (telemetry, time series, logs, sensor data, etc.)
• Familiarity with cloud platforms and modern data stacks (e.g., Azure, Kubernetes, feature stores, ML pipelines)
• Experience with time-series telemetry, simulation data, race strategy, or vehicle performance analysis is strongly preferred
• Ability to quickly learn and reason about race engineering concepts and constraints (e.g., setup, tire strategy, fuel, aero, regulations)
• Proven ability to lead without authority, influence technical direction, and drive outcomes across teams
• Strong written and verbal communication skills, including the ability to present to engineering leaders and executives
• Track record of mentoring and developing other data scientists

• Master’s or Ph.D. in Computer Science, Data Science, Statistics, Electrical/Mechanical Engineering, Applied Mathematics, or a related quantitative field
• equivalent experience considered
• Prior experience in motorsports (NASCAR, IndyCar, F1, WEC, etc.) or high-performance automotive programs
• Experience with audio processing, computer vision, or multimodal modeling applied to noisy real-world data
• Hands-on experience with vector search, embeddings, and retrieval-augmented systems
• Experience designing real-time or near-real-time analytics/ML systems supporting operational decisions
• Contributions to internal tooling, open-source projects, or technical publications

This job may be eligible for relocation benefits