Staff Data Scientist - GM Motorsports

General Motors•Port Charlotte, FL

1d•Hybrid

About The Position

The Staff Data Scientist is a senior technical leader responsible for driving the design, delivery, and adoption of advanced analytics and machine learning solutions across NASCAR, IndyCar, and other GM racing programs. This role sits within Motorsports Product and Machine Intelligence and partners closely with race engineering, vehicle performance, strategy, and IT to turn data into competitive advantage on and off the track. You will set technical direction for data science in motorsports, mentor junior and senior data scientists, and own high-impact initiatives from concept through production and operationalization.

Requirements

Master’s or Ph.D. 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.

Nice To Haves

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.

Responsibilities

Technical leadership 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.
Project ownership & delivery 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.
Motorsports domain impact 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.
Team development & mentorship 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.
Collaboration & communication 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.
Standards & governance 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.