Sr. Data Science, Ops Decision Systems

Rivian•Palo Alto, CA

1d•Onsite

About The Position

Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract. As a company, we constantly challenge what’s possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations. This is a technical individual contributor role that designs, builds, and operates the operations-side modeling and simulation systems for Rivian’s remarketing business: inventory allocation, reconditioning capacity, disposition timing, logistics, and operating expense. The role develops multi-variable simulation and optimization models in Python and Databricks within Git-versioned repositories with code review, automated testing, and CI/CD, and translates operational levers into dollar-denominated outcomes. The Sr. Data Science, Ops Decision Systems role combines applied data science, analytics engineering, and operations ownership: the role both engineers the simulation systems and is accountable for the quality of the operational decisions they inform. Success is measured by the technical robustness of the systems built and the integrity of the plans they produce.

Requirements

Proficiency with Python, SQL, and Databricks (or equivalent warehouse/lakehouse platform); experience with dbt or equivalent transformation frameworks.
Experience with Git-based engineering workflows, code review, and CI/CD pipelines (GitLab or equivalent).
Demonstrated experience owning production data infrastructure end-to-end, including data modeling, pipeline orchestration, testing, and deployment.
Demonstrated ability to design and validate applied simulation and optimization models, including capacity modeling, operational optimization, or multi-variable simulation over multi-year horizons.
Experience reasoning about supply/demand constraints, depreciation mechanics, holding costs, and operating expense, and translating operational decisions into dollar-denominated outcomes.
Demonstrated ability to translate ambiguous operational questions into production data products and durable models.

Nice To Haves

Bachelor’s degree or higher in a quantitative or technical field (Computer Science, Data Science, Statistics, Mathematics, Industrial Engineering, or similar).
Experience applying machine learning or deep learning methods to capacity, logistics, or operational forecasting problems.
Experience integrating external APIs and third-party data sources into production data systems.
Experience with AI-assisted development workflows and agentic coding tools.
Experience in automotive, marketplace, e-commerce, supply chain, or adjacent operations domains.
Familiarity with BI and analytics tools such as Hex, Looker, or equivalent.

Responsibilities

Design, build, and operate production simulation and optimization systems. Develop Python-based simulation models in Databricks as a member of a highly technical team designing interconnected models. Work in Git-versioned repositories with merge-request review, automated testing, and CI/CD pipelines (GitLab), and apply AI-assisted and agentic development workflows as a standard part of the engineering stack.
Statistical and optimization model development. Design, validate, and maintain the models that drive operational decisions: reconditioning capacity and throughput models, operating-expense models, inventory allocation optimization, and disposition-timing models. Apply statistical, machine learning, and optimization methods, with backtesting and production performance monitoring.
Operations data products and pipelines. Build and maintain the data models and pipelines that describe operational performance, covering inventory state, auction outcomes, reconditioning throughput and cost, logistics, and allocation, with data contracts, tests, and documentation that allow downstream decision systems and planning tools to consume them reliably.
AI-augmented engineering. Apply AI-assisted and agentic development workflows as a first-class part of the engineering stack. Evaluate and integrate AI tooling into production engineering workflows and set the patterns the team follows.
Network and capacity scenario engineering. Build and run multi-variable scenario models that optimize the physical infrastructure footprint, vehicle movement strategies, reconditioning capacity plans, and operational workflows across Remarketing operations. Vary levers systematically and narrow many candidate plans to defensible recommendations.
Financial efficiency optimization. Model and trend resource-efficiency outcomes across all areas of operating expense, including reconditioning, storage capacity and utilization, and vehicle movements, and translate operational decisions into projected P&L outcomes over multi-year horizons.
Supply deployment with business partners. Model the prioritization of units for reconditioning, the routing of vehicles toward demand, and the strategic deployment of inventory to maximize profit and stability. Work with customer-focused colleagues to integrate demand signals, and operationalize recommendations with Remarketing operations leadership, internal service and delivery partners, and external third-party partners.