General Motors
Motorsports Sportscar Data Analyst
General Motors - Raleigh, North Carolina, United States
Work at General Motors
Overview
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Overview
Hybrid:
This role is categorized as hybrid. This means the successful candidate is expected to report to Concord, NC three times per week, at minimum. Given the need to support some live race events during weekends, this role will require the ability to work a flexible schedule. The Role
We are seeking a Motorsports Sportscar Data Analyst to apply data science and modeling techniques to develop and refine race strategy tools, facilitating insightful pre-race planning and real-time identification of pivotal strategy inflection points during live race events. What You’ll Do
Evolve existing AI/ML models for application in Sportscars: refine existing Python codebases where appropriate and reimagine where necessary.
Develop a platform for running and interrogating Monte Carlo free-air simulations.
Create tire degradation models, refining parameters and coefficient fits for accurate future predictions.
Conceptualize the most efficient means of conveying insights through new visualizations. Prototype working examples and collaborate with IT and software partners to develop production implementations.
Employ race strategy tools and standalone models to verify accuracy, understand performance, and identify areas for future enhancement. Collaborate with IMSA and WEC teams to implement tools and derive insights for each race event. Resolve true vehicle pace considering track migration, traffic, energy savings strategies, tire and fuel burn effects.
Predict in-race and session-to-session pace variation to inform session planning and optimize strategy approaches.
Collaborate with diverse technical teams across GM, GM Motorsports, and GM-sponsored motorsports teams to develop and support race strategy tools and provide analytical guidance. Co-develop production race strategy software with data science and IT software development teams.
Work with teammates supporting other racing series to apply analysis techniques and modeling approaches to validate and refine processes across series.
Transfer learning and approaches between the series in which GM Motorsports teams compete.
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This role is categorized as hybrid. This means the successful candidate is expected to report to Concord, NC three times per week, at minimum. Given the need to support some live race events during weekends, this role will require the ability to work a flexible schedule. The Role
We are seeking a Motorsports Sportscar Data Analyst to apply data science and modeling techniques to develop and refine race strategy tools, facilitating insightful pre-race planning and real-time identification of pivotal strategy inflection points during live race events. What You’ll Do
Evolve existing AI/ML models for application in Sportscars: refine existing Python codebases where appropriate and reimagine where necessary.
Develop a platform for running and interrogating Monte Carlo free-air simulations.
Create tire degradation models, refining parameters and coefficient fits for accurate future predictions.
Conceptualize the most efficient means of conveying insights through new visualizations. Prototype working examples and collaborate with IT and software partners to develop production implementations.
Employ race strategy tools and standalone models to verify accuracy, understand performance, and identify areas for future enhancement. Collaborate with IMSA and WEC teams to implement tools and derive insights for each race event. Resolve true vehicle pace considering track migration, traffic, energy savings strategies, tire and fuel burn effects.
Predict in-race and session-to-session pace variation to inform session planning and optimize strategy approaches.
Collaborate with diverse technical teams across GM, GM Motorsports, and GM-sponsored motorsports teams to develop and support race strategy tools and provide analytical guidance. Co-develop production race strategy software with data science and IT software development teams.
Work with teammates supporting other racing series to apply analysis techniques and modeling approaches to validate and refine processes across series.
Transfer learning and approaches between the series in which GM Motorsports teams compete.
#LI-LP2
#J-18808-Ljbffr