<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> As a Data Scientist (all genders) you are a key member of the Data Science team, responsible for creating developing data-driven products/solutions and building Machine learning/AI algorithms to improve sales, generate efficiencies and optimize existing processes across Eurowings commercial disciplines.<br/><br/><strong>Tasks<br/><br/></strong><strong>What You'll Do<br/><br/></strong><ul><li>Design, develop, and deploy machine learning and AI solutions that solve commercial problems across pricing, demand, and revenue optimization</li><li>Own model quality end-to-end — monitor performance, improve accuracy, ensure explainability, and quantify business impact throughout the model lifecycle</li><li>Collaborate with Business Analysts, Data Engineers, Revenue Management, and Subject Matter Experts to deliver production-grade data products</li><li>Identify opportunities where emerging techniques (GenAI, advanced forecasting, optimization) can create measurable business value — and build them<br/><br/></li></ul><strong>Our Tech Stack: PySpark, Python</strong><strong>, Databricks, Azure Cloud, MLflow<br/><br/></strong><strong>Requirements<br/><br/></strong><strong>What you'll need<br/><br/></strong><ul><li>University degree (preferably Master's) in Economics, Computer Science, or a STEM-related field</li><li>4–5 years of professional experience as a Data Scientist or ML Engineer</li><li>Solid understanding of Python, SQL and core data science libraries</li><li>Familiarity with BI tools</li><li>Proficiency with Databricks or similar lakehouse/notebook platforms</li><li>Ability to select and justify modeling approaches based on data characteristics, business constraints and interpretability requirements</li><li>Understanding of model performance trade-offs and hyperparameter tuning</li><li>Experience with feature engineering, data cleaning and managing data leakage and overfitting</li><li>Experience with constrained optimization models</li><li>Experience building RAG pipelines or integrating LLMs into production workflows</li><li>At least 2–3 years of experience with MLOps practices and experiment tracking</li><li>Experience working with at least one cloud platform<br/><br/></li></ul><strong>What You'll Bring<br/><br/></strong><ul><li>Curiosity to dig into unfamiliar business domains and ask "why"</li><li>A bias toward pragmatic solutions</li><li>Comfort navigating ambiguity</li><li>Ownership of data quality<br/></li></ul> </div>