Data Scientist (all genders)
Role details
Job location
Tech stack
Job description
As a Data Scientist (all genders) you are a key member of the Data Science team, responsible for creating analytical insights, developing data-driven products/solutions and building Machine learning/AI algorithms to increase efficiencies and productive in an airline Operations environment.
As a Data Scientist (all genders) you are a key member of the Data Science team, responsible for creating analytical insights, developing data-driven products/solutions and building Machine learning/AI algorithms to increase efficiencies and productive in an airline Operations environment. Tasks
What you'll do
- Create data products and deliver insights that support the goals and ambitions of Eurowings Digital and Eurowings Group Operations
- Identify, evaluate, forecast, and provide recommendations on the optimization of Ops processes including aircraft, airport and crew areas
- Work constantly on the accuracy, reliability and success rate of our Ops data products. Monitor and optimize our data products / MVPs on a regular basis
- Work closely with Business Analysts, Product Owners, Data Analysts, Data Engineers and other colleagues to deliver the best data products and processes end-to-end
- Keep track of the latest trends of the data science toolbox. Make our data products better by applying these new trends
Our Tech Stack: PySpark, Python, Databricks, Azure Cloud, MLflow Behind the scenes
Meet the Eurowings Digital Team!
We are dreamers, doers, and enthusiast!
Requirements
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University degree in Computer Science, Mathematics, Economics or any STEM related field
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3-5 years professional experience as a Data Scientist
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Solid understanding of Python, SQL (complex joins, window functions) and core data science libraries
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Can independently train, validate and interpret regression, classification and time series models
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Able to assess performance, understand trade-offs (e.g. precision vs recall) and perform basic hyperparameter tuning
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Comfortable with feature engineering, data cleaning and managing data leakage and overfitting
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Familiarity with evaluation metrics for classification, regression, and time series
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Experience with ML techniques including decision trees, random forests, logistic regression, and time series forecasting
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Experience with MLOps practices
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Proficiency with SHAP and/or LIME for model explainability
What you'll bring
- Solid stakeholder and communication skills
- Strong prioritization and self-management skills
- The ability to think strategically about business, product and technical challenges
