ATLAS Specialist

GM Performance Power Units•Concord, NC

5d•Onsite

About The Position

GM Performance Power Units is seeking a highly motivated Reliability Data Analyst to support the development, testing, and trackside operations of our Formula 1 Power Unit. Based in Concord, North Carolina, this role offers a unique opportunity to contribute to a foundational area of our F1 program and assist in building a world-class reliability team. The Data Analyst will work at the intersection of data engineering and reliability analysis, building dashboards, pipelines, and statistical models that allow the reliability team to understand component health, detect failure trends early, and make data driven decisions, leading to race wins for our advanced hybrid power unit, targeted for debut with the Cadillac F1 Team.

Requirements

Bachelor's degree in Mechanical Engineering, Automotive Engineering, Aerospace Engineering, or a closely related engineering discipline. Degrees in Electrical Engineering or Computer Science considered where accompanied by strong powertrain domain experience.
3–6 years of engineering experience in a data-intensive role within motorsport, aerospace, or high-performance automotive powertrain development.
Demonstrated experience using Motion Applied ATLAS as a primary analysis tool — configuring workbooks, writing math channels, building condition monitors, and conducting detailed multi-channel telemetry analysis. ATLAS proficiency is a hard requirement for this role.
Prior exposure to reliability, test, or powertrain development engineering where data interpretation directly influenced engineering decisions.
Engineering understanding of high-performance ICE systems sufficient to interpret what temperature, pressure, speed, and knock channel behavior indicates about component condition and failure risk.
Proficiency in Python for data processing, automation, and analysis scripting; familiarity with Pandas, NumPy, and matplotlib or equivalent libraries.
Experience with data visualization tools (Power BI, Tableau, or equivalent) for building engineering-facing reliability dashboards.
Familiarity with F1 PU telemetry data structures, channel naming conventions, and the operational context in which data is generated across dyno, test, and race environments.
Able to bridge the gap between data and engineering.
Translate what the numbers mean in terms of hardware behavior, and communicate that to engineers who need to act on it.
Comfortable operating in a fast-paced, deadline-driven environment where post-session analysis turnaround times are measured in minutes or hours, not days.
Willing to work off-hours for race events and test sessions

Nice To Haves

Prior experience in an F1 or top-tier motorsport PU environment with direct exposure to reliability or performance data analysis at the factory and trackside.
Familiarity with ERS/hybrid subsystem data channels and the failure modes they can indicate.
Experience with statistical reliability methods: Weibull analysis, Mean Time Between Failures (MTBF) modeling, fault rate trending, or anomaly detection applied to engineering datasets.
Exposure to FMEA processes, failure investigation workflows, or component life management programs in a regulated motorsport environment.
Scripting experience beyond Python; MATLAB, SQL, or equivalent for data pipeline work or integration with PLM/ERP data sources.

Responsibilities

Serve as the team's primary ATLAS expert.
Configure and maintain workbooks, displays, and automated analyses used by the reliability engineering team across dyno, factory test, and race event environments.
Examine PU telemetry data in ATLAS to identify reliability-relevant anomalies, overload events, and early-warning signatures across ICE, turbocharger, MGU-K, and control electronics channels.
Develop and maintain ATLAS functions and condition monitors that automate the detection of known failure precursors and flag out-of-limit behavior for engineering review.
Apply engineering judgment to data analysis.
Understand what anomalies in pressure, temperature, speed, or ERS channels imply about underlying component behavior, and communicating those implications to the reliability engineering team in context.
Support failure investigations by pulling and processing the relevant data history for failed or suspect components, correlating telemetry evidence with physical teardown findings to build a complete picture of each failure event.
Contribute to reliability trend analysis across the season, tracking fault rates, overload events, and anomaly frequency by subsystem and component to support proactive risk management.
Build and maintain reliability dashboards and post-session reports that give the engineering and operations teams a timely, accurate view of PU health, fault status, and component life consumption.
Produce post-event reliability data summaries following each dyno session and race weekend, distributing findings to the team and flagging any items requiring action before the next event.
Develop Python or equivalent scripting to automate recurring data processing tasks, reducing manual workload and improving report consistency and turnaround time.
Remotely support race events and test sessions as part of the reliability team, operating ATLAS in mission control environments to monitor live PU data and support real-time reliability assessment.
Work alongside the trackside PU and systems engineers to provide data-driven input during sessions, contributing to fault diagnosis and operational limit decisions under race weekend time pressure.