Motorsports Tire Intelligence Scientist

General Motors • Concord, NC

3d • Hybrid

About The Position

The Motorsports Tire Intelligence Scientist develops data and analytics solutions that turn tire, vehicle, testing, simulation, and race data into actionable engineering insight. This individual contributor partners with Tire Science, data engineering, data science, vehicle dynamics, data platform teams, and motorsports stakeholders to improve tire design decisions, understanding of tire operating windows, and event execution.

The role leads technically complex assignments, influences across functions through expertise and execution, and builds repeatable tools and workflows that support engineering decisions before, during, and after race events. The position is categorized as hybrid, with the selected candidate expected to report to a specific location at least three times a week or at another frequency dictated by their manager. Relocation benefits are available to candidates who qualify under company policy.

Requirements

• Bachelor’s degree in data science, statistics, computer science, mechanical engineering, automotive engineering, applied mathematics, or a related technical field.
• At least 5 years of postgraduate experience applying data science, advanced analytics, or machine learning to physical systems such as tires, vehicles, chassis, durability, noise, vibration and harshness, or similar domains.
• Experience building analytical models in Python or similar programming environments using modern data science libraries.
• Experience working with complex, multi-source engineering datasets and designing reliable end-to-end data workflows.
• Strong communication skills, including the ability to explain technical findings clearly to engineering and non-data audiences.
• Demonstrated technical leadership in project delivery, stakeholder alignment, and implementation of impactful analytics solutions.

Nice To Haves

• Master’s degree or doctorate in a relevant discipline.
• Experience with tire mechanics, tire performance, vehicle dynamics, or motorsports engineering.
• Experience analyzing time-series telemetry across testing, simulation, and race events.
• Experience with cloud-based data platforms such as Azure or Databricks.
• Experience building dashboards or decision-support tools for engineering users.
• Familiarity with GM engineering processes, motorsports program execution, or series-level technical requirements and regulations.

Responsibilities

• Build and maintain reliable workflows that ingest, clean, align, and validate tire data from laboratory, rig, cleat, simulation, track, telemetry, and post-event sources.
• Combine structured and unstructured engineering data into analysis-ready datasets for tire performance studies and model development.
• Partner with information technology, software, and data platform teams on data architecture, access, governance, traceability, quality, and documentation.
• Develop and apply statistical, artificial intelligence, and machine learning models to predict tire behavior, including grip evolution, wear, degradation, durability, warm-up behavior, rolling resistance, and event performance.
• Apply time-series analysis, feature engineering, uncertainty quantification, and interpretable modeling to noisy, sparse, and variable motorsports data.
• Work with Tire Science, data engineering, data science, vehicle dynamics, and performance teams to translate engineering questions into scalable analytical solutions.
• Analyze tire operating windows, pressure and setup sensitivity, degradation trends, and the tire contribution to lap time, stint length, and strategy outcomes.
• Deliver dashboards, notebooks, automated reports, and decision-support tools for test reviews, event preparation, race support, post-event assessments, and trend tracking.
• Standardize common analyses and create user-friendly workflows that reduce manual data handling and accelerate insight generation for trackside and remote teams.
• Collaborate with tire suppliers, motorsports partners, and internal stakeholders on technical data interfaces, standards, test methods, performance trends, and operating recommendations.
• Lead project scoping, analytical method selection, stakeholder alignment, and delivery planning for high-value analytics efforts.
• Mentor engineers and analysts in data-driven problem solving, model interpretation, reproducible workflows, and engineering communication.
• Establish best practices for coding, model validation, reproducibility, and technical communication.

Benefits

• Relocation benefits are available for candidates who qualify under company policy.
• GM describes benefits and resources intended to support employees’ well-being at work and at home; specific benefits are not listed.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior