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# Senior Data Scientist

- **Company:** kununu
- **Location:** Wien, Austria (Remote available)
- **Experience:** Expert
- **Salary:** €60,000.0 - €82,000.0
- **Contract:** Permanent contract
- **Skills:** Code Reuse, Code Review, Data Warehousing, Python (Programming Language), Machine Learning, Standard Sql, Unstructured Data, Reinforcement Learning, Large Language Models, Build Management, Software Version Control
- **Published:** September 13, 2026
- **Apply:** https://www.xing.com/jobs/vienna-senior-data-scientist-157800893

## About the Role

* Several years of hands-on data science experience, including at least one recommendation or personalization system you brought to production.
 * A strong foundation in statistics and machine learning. Practical experience with multi-armed bandit experimentation or reinforcement learning is a big plus.
 * You are comfortable across classical machine learning problems such as classification, ranking and scoring, not only recommendations.
 * Real production experience with LLM-based applications: agentic tools, LLM judges and evaluation harnesses.
 * Solid Python and SQL skills. You are comfortable in a modern data warehouse and with transformation tools such as dbt.
 * You write clean, tested and reusable code, and you see version control and code reviews as a normal part of your work.
 * You explain complex results in simple terms to people outside data science, and you enjoy mentoring and giving constructive feedback.

## Description

* Drive the evolution of our recommendation landscape and shape how people discover employers, jobs and content on kununu.
 * Own the full data science process: from framing the problem and exploring the data to modelling, evaluation and production rollout.
 * Apply methods such as bandits and reinforcement learning to personalize content in a live, high-traffic environment.
 * Design and build LLM-driven, agentic applications on top of a large body of unstructured data, and set up solid evaluation for them, including LLM-as-a-judge approaches.
 * Work closely with product and engineering to turn business questions into data science problems and bring your models into the product.
 * Define with product what success looks like: choose the right metrics and evaluation strategy, run experiments, and turn hypotheses into empirical results within weeks.
 * ML-Lifecycle: Work with transformation tooling to build reproducible data foundations for your models, monitor quality and cost, and improve them over time.
 * Mentor more junior colleagues in the team: review their work, share your knowledge and help them grow.
 * Act as a sparring partner for the team lead on statistical methods, machine learning concepts and solution design.


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