Senior Data Scientist, AV and ADAS Insights

The Role

General Motors is building the next generation of software-defined vehicles and advanced driver-assistance experiences. The success of these products depends on understanding how they perform in the real world, how customers experience them, and where product and engineering investment will create the greatest benefit.

As a Senior Data Scientist, Super Cruise (SC) and Assisted Driving and Active Safety (ADAS) Insights, you will be a hands-on technical and strategic partner to Product Management, Systems Engineering, Data Engineering, Safety, and Program teams. You will turn complex vehicle telemetry, retail-fleet data, engineering data, and customer-behavior signals into trusted metrics, actionable insights, and clear recommendations that shape product strategy and prioritization.

You will help product teams understand feature availability, usage, evaluate feature availability, utilization, safety performance, reliability, customer acceptance, and trust-related behaviors across Super Cruise, advanced autonomy products and ADAS products.

This is a senior individual-contributor role for someone who can independently frame ambiguous problems, develop rigorous analyses, influence decisions without formal authority, and establish analytical practices that scale across the organization.

What You’ll Do

• Partner with Product Management to translate product questions into analyses that inform strategy, roadmaps, requirements, prioritization, investments, and launch decisions.

• Define and maintain trusted KPI frameworks for AV, Super Cruise and ADAS, including metric definitions, assumptions, data lineage, limitations, baselines, thresholds, and appropriate use of engineering and retail-fleet data.

• Evaluate product performance across availability, usage, effectiveness, customer value, and experience, identifying drivers, tradeoffs, risks, opportunities, regressions, and regional or population-level differences.

• Build integrated datasets, models, dashboards, scorecards, recurring reports, and self-service tools that connect vehicle, driver, safety-event, trip, crash, and operating-context data to support ongoing monitoring and action.

• Investigate unexpected trends and data-quality issues, partnering with engineering and data teams to address gaps in signals, triggers, decoding, sampling, instrumentation, and data availability.

• Apply sound statistical and causal-inference methods to vehicle data, evaluations, feature rollouts, and constrained experiments, and communicate findings and recommendations effectively across technical teams, cross-functional forums, and senior leadership.

How You’ll Make an Impact

• Product teams use a common, trusted view of performance rather than disconnected or conflicting analyses.

• Retail-fleet data becomes a practical input to product strategy, release decisions, regional expansion, and engineering prioritization.

• High-value customer and safety problems are quantified, ranked, and connected to specific product or engineering actions.

• Product teams can distinguish true performance changes from changes in data coverage, instrumentation, fleet mix, software releases, or analytical definitions.

• New metrics and data use cases are developed with appropriate privacy, access-control, regulatory, and data-retention considerations.

• Decision-makers receive clear narratives that explain what changed, why it matters, what is uncertain, and what should happen next.

• 5 or more years of experience in data science, product analytics, applied statistics, or a closely related field.

• Expert-level SQL skills and strong experience working with large, complex, evolving data environments.

• Strong Python skills for data preparation, exploratory analysis, statistical analysis, visualization, automation, and reproducible analytical workflows.

• Demonstrated experience defining metrics, validating datasets, identifying data-quality issues, and explaining analytical limitations.

• Experience using observational data to evaluate product performance, customer behavior, feature adoption, reliability, safety, or operational outcomes.

• Demonstrated ability to translate ambiguous product or business questions into rigorous analysis and actionable recommendations.

• Experience influencing product roadmaps, prioritization, investment decisions, launch decisions, or requirements through data and analysis.

• Strong written and verbal communication skills, including the ability to explain technical concepts and uncertainty to non-technical stakeholders.

• Ability to operate with substantial autonomy, exercise sound judgment, and deliver results across multiple teams without direct reporting authority.

• Experience working with automotive, connected-vehicle, ADAS, autonomous-driving, robotics, mobility, or other safety-critical products, including vehicle telemetry, sensor and fleet data, operational statistics, event recording, and driver-assistance systems such as Super Cruise.

• Experience with Databricks, Spark, Azure, GCP, Power BI, Tableau, Looker, or comparable data and visualization platforms.

• Experience with metric catalogs, data contracts, data governance, instrumentation strategy, data sampling, human labeling, or analytical quality standards.

• Experience with causal inference, quasi-experimental methods, rollout analysis, survival or reliability analysis, hierarchical modeling, or other methods appropriate for real-world product data.

• Experience working with privacy, legal, regulatory, or data-access constraints in the development of customer or vehicle-data use cases.

What Success Looks Like in the First 12 Months

• Establish a trusted analytical partnership with SC Product Management and Systems Engineering.

• Deliver a prioritized set of SC performance insights that directly informs roadmap or investment decisions.

• Improve the definition, validation, or usability of key SC topline and trust-eroding-behavior metrics.

• Establish repeatable release-over-release and retail-fleet performance analyses for agreed priority metrics.

• Contribute analytical evidence to at least one launch, regional expansion, or major software-release decision.

• Identify material gaps in data, instrumentation, sampling, or metric quality and drive practical resolutions with partner teams.

• Create reusable analytical assets that reduce ad hoc work and improve product-team self-service.

• Extend the approach to one or more Active Safety or Assisted Driving use cases.