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# Data Scientist

- **Company:** AutoScout24 Nederland B.V.
- **Location:** München, Germany
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Code Review, Python (Programming Language), Tensorflow, Large Language Models, Information Technology, Machine Learning Operations
- **Published:** September 22, 2026
- **Apply:** https://job-boards.greenhouse.io/autoscout24/jobs/7751011003

## About the Role

* Advanced degree in Computer Science, Engineering, Mathematics, or a comparable field

 * 5+ years of experience building and owning production ML systems end-to-end in a real-world environment

 * Strong hands-on expertise in Python and modern ML frameworks, with the ability to ship, operate, and evolve ML systems in production

 * Solid experience with AWS and MLOps practices, including deployment, monitoring, and operating ML systems at scale

 * Practical experience applying GenAI / LLMs in applied or production contexts, with a clear understanding of trade-offs and limitations

 * Ability to influence technical decisions through clear judgment and collaboration, not formal authority

 * Experience supporting and guiding more junior data scientists through hands-on collaboration, code reviews, and technical feedback

 * Strong sense of ownership and accountability for outcomes, with a pragmatic approach to trade-offs and delivery

## Description

You design, build, and operate machine learning and GenAI systems that run in production and matter to the business. You work on complex, ambiguous problems, exercise strong technical judgment, and take responsibility for outcomes - not just models. While this is a hands-on role, you are also expected to influence technical direction, raise the bar for engineering practices, and enable other data scientists and engineers to succeed. This position is permanent and based in Munich.

What you'll do

 * Own ML and GenAI systems end-to-end in production, including problem framing, system design, deployment, monitoring, and continuous improvement

 * Make sound architectural and methodological decisions for ML systems on AWS, balancing robustness, observability, cost, and long-term maintainability

 * Partner closely with Product, Engineering, and Business leaders to turn ambiguous problems into ML-enabled product solutions with clear outcomes

 * Operate and evolve ML systems deployed across multiple countries and markets, accounting for differences in data distributions, regulations, and constraints

 * Mentor other data scientists through hands-on technical guidance, design reviews, and shared ownership of systems


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