Sr. Validation Data Scientist

Sr. Validation Data Scientist Rivian - 2.7 Irvine, CA Job Details Full-time 2 hours ago Qualifications Jira Performance dashboard reports Data model design Dashboard development Data visualization software proficiency Data transformation pipeline development Machine learning/AI-based analysis Statistics Computer Science Data modeling projects Bachelor's degree in statistics Engineering Data reporting Google Cloud Platform 3 years Master's degree SQL Data quality management AWS Outlier detection Bachelor's degree in engineering Bachelor's degree Continuous improvement Design failure mode and effects analysis (DFMEA) Statistical modeling Master's degree in statistics Data interpretation Scalability Master's degree in computer science Senior level Full Job Description Vehicle Validation at Rivian ensures every product delivers exceptional function, quality, reliability, and real-world performance. We perform both verification and validation against data-driven requirements that reflect how our customers use our vehicles—leveraging scalable analytics, integrated data pipelines, and validation governance frameworks to continuously improve how we plan, execute, and learn. As a Senior Data Scientist, Vehicle Validation, you will develop and scale data products, analytics frameworks, and modeling approaches that enable visibility into validation coverage, risk, and readiness from concept through launch and into early field performance. You will partner closely with Validation TPMs, Engineering, Quality, Test & Development, and Data Engineering to transform fragmented validation data into clear, actionable insights—helping ensure the right tests happen at the right time and that leadership has a clear view of validation status and risk. This is a RIV-5 scope role: you will own key analytics workstreams within the validation data ecosystem, operating with guidance from validation and technical leadership while building toward greater end-to-end ownership over time. Develop and maintain validation dashboards, analytics, and reporting that provide visibility into validation coverage, maturity, risk, and residuals across programs. Build and support data models that connect requirements (RL0-RL3), DFMEA / NUDDs, DVPs, test execution, and issue tracking into a cohesive validation data framework. Create and maintain data pipelines that ingest and structure data from systems such as Jira, test logs, vehicle telemetry, and other internal tools. Apply statistical and machine learning techniques (e.g., trend analysis, forecasting, anomaly detection) to identify risks, gaps, and opportunities within validation data. Partner with Validation TPMs and cross-functional teams to translate data into actionable insights that support program execution and decision making. Support the definition and tracking of validation KPIs (e.g., coverage, confidence, defect trends, closure rates, readiness metrics). Contribute to validation governance by enabling consistent, reliable, and accessible data across builds and milestones (DV PV SOP+). Identify data gaps or inconsistencies and work with stakeholders to improve data quality, structure, and usability. Contribute to continuous improvement of validation data infrastructure, tools, and processes to increase efficiency and scalability. Typically 3-6 years of experience in data science, analytics, or related roles within automotive or other complex hardware/software products. Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Engineering, or a related technical field. Experience building data pipelines and analytics solutions (e.g., Python, SQL, and cloud platforms such as AWS or GCP). Experience developing dashboards and visualizations (e.g. Hex, or similar) to support operational decision making. Experience working with large, multi-source datasets and building structured data models. Familiarity with machine learning techniques and their practical application to real-world problems. Strong collaboration and communication skills, with the ability to work across technical and non-technical teams. Comfortable working in ambiguous environments with a willingness to learn new domains and systems quickly. Passion for improving product quality, reliability, and customer experience through data.