About the company
As the world’s pioneering local delivery platform, our mission is to deliver an amazing experience, fast, easy, and to your door. We operate in around 65 countries worldwide, powered by tech, designed by people. As one of Europe’s largest tech platforms, headquartered in Berlin, Germany, Delivery Hero has been listed on the Frankfurt Stock Exchange since 2017 and is part of the MDAX stock market index. We push hard, learn quickly and stay human along the way. It’s this wonderful mix of high performance and real community that makes Delivery Hero a place where ambition and belonging grow side by side. If you’re curious, collaborative and ready to dive deep into meaningful work, you’ll fit right in.
Responsibilities
- Design & Innovate: Lead the conceptualization and design of advanced modeling solutions (using a mix of ML, Statistics and Math) to tackle complex, large-scale Logistics challenges, ensuring alignment with business value and stakeholder needs.
- Build & Automate: Take ownership of developing, implementing and automating robust data science models and pipelines, including feature generation, retraining, and prediction deployment, leveraging tools like BigQuery and Spark as needed.
- Solve & Collaborate: Actively drive technical discussions and problem-solving sessions, contributing significant expertise and innovative ideas to overcome the toughest modeling hurdles alongside your Data Science peers and squad lead.
- Drive Business Impact: Translate sophisticated analytical findings and model outcomes into clear, actionable insights for stakeholders, demonstrably driving value and improvements in key business metrics.
- Shape Data Strategy: Proactively engage with Data & ML Engineering and Tech teams to define critical data requirements and influence technical roadmaps, ensuring the necessary data infrastructure for impactful projects.
- Contribute & Elevate: Make a significant impact within our fast-paced, global organization by delivering high-quality solutions, sharing knowledge, and contributing to the team's overall growth and best practices.
Requirements
- Applied Machine Learning Mastery: You have significant experience (ideally 3+ years) designing and implementing diverse machine learning models (e.g., regression, classification, tree-based ensembles) to solve real-world business problems, backed by a strong theoretical foundation in statistics and probability.
- Production ML Engineering: You possess expert-level Python skills (incl. pandas, scikit-learn, etc.), proficiency in SQL, and demonstrable experience building robust, maintainable, and automated ML pipelines using Big Data technologies (e.g., BigQuery, Spark) and workflow orchestration tools (e.g., Airflow, Meta...).