Data Scientist in high-traffic search ranking

Delivery Hero operates a local delivery platform in around 65 countries, providing customers with fast, convenient delivery. Its search functionality spans more than 60 countries and 35 languages, facilitating over 80 million searches daily.

• Design, build, and productionize deep neural ranking models, owning their full lifecycle from offline experimentation and evaluation to monitoring and iterative improvement
• Use LLM coding agents to autonomously iterate on feature engineering and Learning to Rank architectures; design overnight experiment pipelines and review their outputs
• Innovate in feature engineering and model architecture by applying ranking signals, feature stores, and embedding-based retrieval, and validate new architectures based on the latest research
• Monitor production model health, including model drift, latency degradation, and feature pipeline integrity, and respond to deviations
• Collaborate with Backend Engineers, Data Engineers, and Product Managers on end-to-end improvements; explain ML trade-offs to non-technical stakeholders and contribute to the team roadmap
• Mentor junior and mid-level data scientists, promote experimentation rigor and code quality, and contribute to learning about agentic development workflows

• Master's degree in Computer Science, Mathematics, Physics, or a related quantitative field, or a Bachelor's degree with 6+ years of work experience
• 4+ Years of industry experience as a Data Scientist or Machine Learning Engineer applying ML in high-traffic production environments
• Production experience implementing deep learning ranking architectures, including DCN-V2, MMoE, and Two-Tower; knowledge of multi-task learning, cross-feature interactions, and embedding optimization at scale
• Knowledge of Learning to Rank methods, offline evaluation metrics such as NDCG and MRR, and the challenges of translating offline metrics into online A/B outcomes
• Proficiency with LLM coding agents and agentic CLI tools for autonomous experiment generation, architecture prototyping, and rapid iteration; familiarity with the auto-research paradigm and reviewing large volumes of experiment results
• Expertise in Python and its ML ecosystem, including PyTorch, TensorFlow, and scikit-learn
• Experience with SQL, dbt, GCP or AWS, and large-scale data processing with PySpark or Scala
• Knowledge of the ML lifecycle, including feature stores, vector databases, model versioning, A/B experimentation frameworks, and deployment to high-throughput, low-latency serving infrastructure
• Hands-on MLOps experience with CI/CD for ML pipelines, Metaflow, experiment tracking, model registries, and production monitoring for drift, latency, and data quality
• Ability to navigate ambiguity, translate business objectives into scoped data science projects, and take accountability for outcomes
• Ability to collaborate in globally distributed, cross-functional teams and communicate model behavior, experiment results, and trade-offs to technical and non-technical audiences

• 27 Days of annual leave, plus one extra day in the second and third years of service
• €1,000 Educational budget, language courses, parental support, and access to Udemy Business
• Health checkups, meditation, and gym access
• Employee Share Purchase Plan, Sabbatical Bank, public transportation ticket discount, life and accident insurance, and corporate pension plan
• Digital meal vouchers and food vouchers
• Relocation information and support resources for moving to Berlin are available
• Severely disabled applicants with equal qualifications will be given preferential consideration