Staff Product Data Scientist, Vehicle Analytics & Insights

General Motors•Mountain View, CA

1d•$159,400 - $245,700•Remote

About The Position

At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard —from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features. Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale. The Role We are building a new Vehicle Analytics & Insights team within GM’s Vehicle Product organization, and this role is a foundational hire. As a Staff Product Data Scientist, you’ll help define how analytics informs product decisions for GM’s vehicle experience. You’ll work at the intersection of vehicle data, product strategy, and user behavior—turning complex telemetry and product signals into clear, actionable insights. Your work will help product teams move beyond intuition to a quantitative understanding of how customers use, adopt, and experience vehicle features at scale. You’ll partner closely with product managers, engineers and designers and play an early role in shaping product analytics standards, metrics, and how insights are used across the organization. This role offers meaningful ownership and visibility, with the opportunity to shape how product analytics drives decisions and scales across GM as the capability grows.

Requirements

Master’s degree in Analytics, Statistics, Mathematics, Computer Science, or a related quantitative field (or equivalent practical experience).
7+ years of experience in data science or analytics, with a strong preference for product analytics / product data science roles supporting software or digital products.
Expert‑level SQL skills, with a demonstrated ability to define trusted metrics, validate data, and diagnose data quality issues in complex, evolving data ecosystems.
Strong experience using Python (or similar languages) for exploratory analysis, statistical modeling, experimentation, and automation.
Demonstrated ability to translate data into clear stories and recommendations, and to communicate technical concepts effectively to audiences with varying levels of technical depth.
Hands-on experience with data visualization and BI tools (e.g., Databricks, Power BI, Looker, or similar) and a strong point of view on what makes dashboards useful versus misleading.
Strong written and verbal communication skills, with a proven ability to collaborate effectively across Product, Engineering, Design, and adjacent teams.

Nice To Haves

Experience designing and analyzing experiments or feature rollouts, including an understanding (and/or application) of causal inference and real-world constraints.
Track record of driving impact beyond a single team or product, influencing how multiple teams approach measurement, decision‑making, or experimentation.
Comfortable working in ambiguous, early-stage environments, where processes are still being defined and impact comes from building the foundation as much as delivering immediate insights.

Responsibilities

Define, standardize, and evolve product success metrics and KPI frameworks across in‑vehicle experiences, ensuring consistency, clarity, and alignment to customer value and business outcomes.
Lead the product analytics instrumentation strategy, partnering with Product and Engineering to define what data should be captured, how it should be implemented, and how quality and consistency are maintained.
Act as a thought partner to product teams to proactively identify opportunities and inform prioritization and product strategy, rather than responding to ad hoc requests.
Perform deep-dive analyses to understand customer journeys, engagement patterns, friction points, and value moments across in-vehicle experiences, translating complex behavioral data into clear, actionable insights.
Design, build, and own dashboards and self-serve analytics that enable product teams and leaders to continuously monitor product performance, adoption, and health.
Frame complex analytical findings into clear decision narratives, explicitly surfacing tradeoffs, risks, and opportunities to influence product strategy and prioritization.
Set best practices for experimentation and causal inference in environments where traditional A/B testing may be constrained, applying statistical rigor and sound judgment to real‑world product decisions.
Help establish the foundational product analytics practices for the Vehicle Product organization that will scale as the team grows.
Act as a force multiplier for analytics capability, mentoring analysts and product partners, raising data literacy, and enabling teams to increasingly self‑serve insights while maintaining high analytical standards.