Senior Quality Dimensional Data Scientist (m/w/d) - Gigafactory Berlin-Brandenburg

Tesla is seeking a Senior Quality Dimensional Data Scientist to join the Quality Dimensional Automation team at Gigafactory Berlin-Brandenburg. The team builds scalable data platforms, near-real-time pipelines, and analytics that improve dimensional quality. This role puts production AI on finished-vehicle gap and flush: every car is scanned at end-of-line for body fit, models correlate that condition with upstream process traces and quality metrics, the deviation is auto-traced to the causing line, team, supplier, or design, and the main causer is flagged so the owner can act immediately. Downstream body fit must drive upstream change on tolerancing and definition. You will partner with Manufacturing Engineering, Quality Engineering, Metrology, Data Engineering, Software Engineering, Supplier Quality, Design, and IT. You own the path from raw metrology ingest to production models, applications, and automated alerts, and you will spend real time on the factory floor for AI validation.

- Design, train, validate, and productionize models that correlate end-of-line and inline gap and flush (3D body scans, body fit, closures, paint-to-body, general assembly) with stamping, body-in-white, metrology, supplier incoming quality, MES/QMS, torque and process traces, and design / GD&T.
- Auto-trace a finished-vehicle dimensional deviation to production line, process station, shift/team, supplier lot or part, and/or design characteristic, with ranked contribution, confidence, and evidence.
- Automatically flag the main causer to the accountable owner, with recommended containment and the next measurement to confirm the fix, so body fit immediately informs stampings, fixtures, and process parameters.
- Monitor model drift, false-flag rate, and time-to-containment; improve attribution as new vehicle, tool, and supplier data arrives.
- Set up end-to-end systems by yourself from data ingest and models through production applications and automated action.
- Lead full-lifecycle data science projects: scope, success metrics, resources, and delivery. Work with large, complex dimensional datasets. Apply statistics, causal inference, computer vision, and deep learning as needed.
- Prototype and deploy models. Own batch and near-real-time pipelines from ingestion to visualization and automated action, including data models for SPC, causal attribution, and quality reporting.
- Architect new as well as own, maintain and improve existing applications with data engineers and software engineers: user-facing tools for Quality, Manufacturing, and Metrology, reusable components, fast prototyping, and query, model, and application performance.
- Operate and support what you ship. Automate repetitive quality-engineering work. Write clear technical documentation.
- Mentor cross-functional project teams. Translate requirements with Manufacturing, Quality, Metrology, Supplier, Design, and IT, and manage trade-offs between technical feasibility and business need.
- Present findings to non-technical stakeholders through clear visualizations. Drive closed-loop action on the line — a flagged causer must be contained, not left in a dashboard.

- Master’s degree in Computer Science, Data Science, Statistics, Mechanical / Manufacturing / Quality Engineering, or equivalent experience (Bachelor’s degree plus a strong industry track record).
- 5+ years in data science or machine learning, including production models that have delivered measurable value.
- Strong Python and SQL; clean, tested software in a professional environment. Additional languages (JavaScript, C/C++, Go, or similar) are a plus.
- Statistics and ML, including causal inference, multivariate methods, computer vision, and/or deep learning.
- ETL/ELT, data modeling, and high-volume manufacturing, sensor, or metrology data. Version control, CI/CD, and DevOps (Docker, Kubernetes, Jenkins, or equivalent).
- Ability to take a vague problem, refine the scope, and drive decisions with stakeholders. English required; German is a plus. Bias for action in a fast-paced manufacturing environment.
- Candidates are expected to uphold and actively promote sustainability principles in their daily work, operating in line with Tesla Global Environmental, Health, Safety & Security (EHS&S) Policy and EMAS requirements, fostering a culture of continuous environmental improvement