Sr. Data Scientist, Demand Decision Systems

Rivian•Palo Alto, CA

About The Position

This is a technical individual contributor role that designs, builds, and operates the demand-side modeling and simulation systems for Rivian’s remarketing business. The role develops production forecasting models for order volume, market sizing, price sensitivity, and customer segmentation, implemented in Python and Databricks within Git-versioned repositories with code review, automated testing, and CI/CD. Model outputs drive sales strategy, product positioning, and demand planning for the Pre-Owned business. The Sr. Data Science, Demand Decision Systems role combines applied data science, analytics engineering, and product ownership: the role both engineers the forecasting systems and is accountable for the quality of the demand 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 building and operating production forecasting or demand models end-to-end, including data modeling, pipeline orchestration, model validation, testing, and deployment.
Demonstrated ability to design and validate applied statistical and machine learning models, including time-series forecasting, demand modeling, segmentation, or elasticity estimation.
Experience in a technical demand-forecasting, marketing science, or sales-forecasting role (such as data science on a marketing team or quantitative forecasting at a sales-driven company).
Demonstrated ability to translate ambiguous business 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 deep learning methods to forecasting, segmentation, or demand-sensing problems.
Experience integrating external APIs and third-party data sources into production data systems.
Experience with AI-assisted development workflows, agentic coding tools, and integrating AI tooling into production engineering processes.
Experience in automotive, marketplace, e-commerce, or adjacent customer-facing domains.
Familiarity with BI and analytics tools such as Hex, Looker, or equivalent.

Responsibilities

Design, build, and operate production forecasting and simulation systems. Develop Python-based simulation and forecasting 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 machine learning model development. Design, validate, and maintain the models that drive demand decisions: time-series order volume forecasting, price sensitivity and willingness-to-pay estimation, customer segmentation, and demand-sensing models. Apply statistical, machine learning, and deep learning methods where appropriate, with backtesting and production performance monitoring.
Demand data products and pipelines. Build and maintain the data models and pipelines that describe demand, covering sales transactions, vehicle configuration, geographic and pricing signals, and customer attributes, 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.
Market sizing and demand-sensing models. Build the models that size the market for used Rivians and competitive vehicles, project market and environment changes, and quantify where and how demand is shifting, including how and where competitors sell used Rivians. Maintain authoritative, versioned market-size and demand baselines consumed by long-range planning.
Supply-demand balancing. Model the interaction between incoming vehicle supply and demand levers to engineer healthy future sales volumes. Work with supply-focused colleagues to integrate supply constraints, and quantify the risk, cost, and expected impact of proposed demand-generation actions through scenario simulation.
Operationalize model outputs with business partners. Translate model outputs into pricing, positioning, and sales-strategy actions with marketing and sales partners, and align with the team on decision-system and long-range planning tool design.