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About the Role
Join the Estimations team to build and improve travel time and distance predictions for a global delivery network. You will apply machine learning and statistical techniques to solve operational challenges, collaborating with ML Engineers and Operations Research Scientists to enhance delivery accuracy and routing efficiency.
Responsibilities
- Develop and validate ML models to improve prediction accuracy for travel times and distances
- Build features from operational data and design experiments to measure model performance
- Apply engineering best practices including clean code, version control, unit testing, and production monitoring
- Translate analytics outputs into actionable insights for stakeholders
- Collaborate with ML Engineers and Operations Research Scientists on cross-functional projects
- Identify opportunities to automate retraining, validation, and evaluation pipelines
- Monitor and maintain predictive performance in live production environments
Requirements
- Hands-on experience building and validating ML models
- Proficiency in Python (pandas, scikit-learn) and SQL
- Understanding of supervised ML techniques including regression, classification, and gradient boosting
- Knowledge of statistical foundations, evaluation metrics, and experimental design
- Exposure to ML Ops practices including Git, data pipelines, and model monitoring
Skills
- Python
- Pandas
- Scikit-learn
- SQL
- Supervised Machine Learning
- Regression
- Classification
- Gradient Boosting
- Statistical Analysis
- Experimental Design
- ML Ops
- Git
- Geospatial Data
- Data Visualization
Location
- Berlin, Germany
Work Type
- Hybrid
- Full-time
About the Company
- Leading global online delivery platform
- Operates in 24 countries with over 60 million active users
- Connects customers with restaurant, grocery, and convenience partners
Equal Opportunity
- Committed to creating an inclusive culture, encouraging diversity of people and thinking, where all employees feel they belong