Sr. Data Engineer, Used Cars Program

What to Expect

Tesla continues to expand rapidly in line with our mission to create a sustainable world of abundance. To keep pace with this growth, the North America Used Cars team is seeking a rigorous, analytically driven problem solver with the depth to operate across modeling, pricing strategy, and operational execution. This team is responsible for the Tesla Used Cars business focused on the acquisition, refurbishment, and resale of all used Tesla and non-Tesla vehicles.

Tesla is seeking a Data Engineer to join the Used Cars Programs team, a high-ownership role sitting at the intersection of machine learning, pricing science, and business operations. The used car pricing function currently supports multiple markets (US, Canada, Puerto Rico, Mexico) with additional markets on the horizon. This role will be instrumental in executing a growing portfolio of ML and analytical initiatives from daily pricing operations and exception handling to new model development and strategic analytical projects.

The right candidate is comfortable building and owning models end-to-end, diving into messy data, and translating analysis into pricing decisions that directly affect margins, inventory turn, and customer outcomes. This is a rare opportunity to do technically rigorous work with immediate, measurable business impact at one of the world's most data-rich automotive companies.

Role can be located in either Palo Alto, CA or Austin, TX.

What You’ll Do

• Own the day-to-day operational backbone of the used car pricing function across all active markets (US, Canada, Puerto Rico, Mexico), maintaining pricing system parameters, handling exceptions, and ensuring outputs remain accurate and competitive
• Develop and improve ML-based pricing models, including resale value forecast, listing price optimization, and trade-in offer models — supporting the full lifecycle from design and validation through production monitoring and continuous improvement
• Execute a high-priority strategic analytics backlog, owning projects end-to-end including third-party pricing analysis, competitor trend tracking and insights, and invetory optimization strategy
• Monitor model performance in production, detecting and addressing drift, edge case failures, and unexpected output patterns, and resolving escalations that require manual inspection or override
• Explore new data science application areas across the Used Cars business, including routing efficiency, auction price strategy, and other optimization problems as the team's ML roadmap expands
• Support market expansion efforts, adapting models and pipelines to new geographies with distinct data environments and regulatory considerations
• Partner cross-functionally with Finance Analytics, Product, Operations, and senior leadership to communicate findings, validate assumptions, and translate complex analytical outputs into clear, actionable recommendations for non-technical stakeholders

What You’ll Bring

• 3–5+ years of professional experience in data science, quantitative analytics, or a related technical field, with demonstrated ability to operate with both technical precision and commercial judgment
• Strong proficiency in Python or R for data manipulation, statistical modeling, and machine learning, paired with solid command of SQL and experience working with large-scale datasets
• Hands-on experience building and deploying predictive models (regression, time series, tree-based methods, or probabilistic models) in a production or near-production setting, including familiarity with model monitoring and drift detection
• Proven ability to translate analytical output into business decisions and communicate findings clearly to non-technical stakeholders, including senior leadership
• Prior experience in pricing, valuation, inventory optimization, or automotive/retail verticals is a strong plus; familiarity with ML experimentation frameworks (e.g., A/B testing, champion-challenger evaluation) is also highly desirable
• Thrives in a high-velocity, ambiguous environment where priorities shift, autonomy is expected, and personal ownership of outcomes is the norm