The Motorsports Tire Intelligence Scientist joins the GM Motorsports team to develop data and analytics solutions that convert tire, vehicle, testing, simulation, and race data into actionable engineering insights. This individual contributor builds and maintains reliable data workflows to ingest and validate information from laboratory, rig, track, and telemetry sources. The role involves developing statistical, artificial intelligence, and machine learning models to predict tire behavior, including grip evolution, wear, and durability. Key responsibilities include applying time-series analysis and feature engineering to noisy motorsports data to support tire design decisions and operating window understanding. Required skills include proficiency in Python, experience with multi-source engineering datasets, and a background in data science or engineering. The work focuses on solving technical problems regarding tire performance across testing, simulation, and live race events.
Build and maintain reliable data workflows to ingest and validate tire data from various sources.
Combine structured and unstructured engineering data into analysis-ready datasets for performance studies.
Develop and apply machine learning models to predict tire behavior, grip evolution, and wear.
Apply time-series analysis and feature engineering to noisy and variable motorsports data.
Translate engineering questions into scalable analytical solutions for tire science and vehicle dynamics teams.
Analyze tire operating windows, pressure sensitivity, and degradation trends to influence race strategy.
Deliver dashboards, automated reports, and decision-support tools for test reviews and race support.
Standardize common analyses and create user-friendly workflows to reduce manual data handling.
What we're looking for
Bachelor’s degree in data science, statistics, computer science, mechanical engineering, automotive engineering, applied mathematics, or a related technical field.
5 or more years of postgraduate experience applying data science, advanced analytics, or machine learning to physical systems.
Experience building analytical models in Python or similar programming environments using modern data science libraries.
Experience building and designing reliable end-to-end data workflows for complex, multi-source engineering datasets.
Strong communication skills 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.
Master’s degree or doctorate in a relevant discipline (preferred).
Experience with tire mechanics, vehicle dynamics, motorsports engineering, or cloud-based platforms like Azure or Databricks (preferred).
Python
MATLAB
C#
SQL
AWS
Microsoft Azure
Kubernetes
MQTT
Grafana
TCP/IP
Ethernet
Wi-Fi
CAN
Wireshark
CANalyzer
HIL
MongoDB
InfluxDB
SQL Server
Linux
Windows Command-Line
Data Streaming