Senior Quality Dimensional Data Scientist - Gigafactory Berlin-Brandenburg

• 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

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