ГерманияDelivery Hero
Senior Data Scientist - (Global Search, Consumer)
8 ч. назад
ГерманияSeniorHybridC1
deep learningpythonpytorchtensorflowscikit-learnsqldbtgcp
Senior Data Scientist role focused on building and productionalizing deep neural ranking models for Delivery Hero's global search platform, operating in Berlin with hybrid work format.
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 enable creative minds to deliver solutions that create impact within our ecosystem.
- We move fast, take action and adapt.
- No matter where you're from or what you believe in, we build, we deliver, we lead.
- We are Delivery Hero.
About the product
- Our search functionality spans more than 60 countries and 35 languages, facilitating over 80 million searches daily across four continents.
- The ranking systems you build will directly shape what millions of customers see every time they open the app.
Responsibilities
- Own the Ranking Stack End-to-End: Design, build, and productionalize deep neural ranking models — including DCN-V2, MMoE, and Two-Tower architectures — operating at high throughput and low latency in production.
- You will own the full lifecycle: from offline experimentation and evaluation to monitoring, and iterative improvement.
- Drive Agentic ML Research: Embrace and champion the shift from manual experimentation to prompt-driven orchestration.
- You will leverage LLM coding agents (e.g., Claude Code, Gemini) to autonomously iterate on feature engineering and Learning to Rank (LTR) architectures.
- Inspired by the auto-research paradigm, you will design overnight experiment pipelines and review the outputs of hundreds of autonomous runs to identify signals quickly.
- Lead Feature Engineering and Model Architecture Innovation: Apply your deep expertise in ranking signals, feature stores, and embedding-based retrieval to push the quality of our rankers.
- Propose and validate new model architectures grounded in the latest research, translating academic advances into production-grade systems.
- Ensure Production Reliability: Maintain rigorous standards for model health in production.
- You will own monitoring for model drift, latency degradation, and feature pipeline integrity, and act quickly when signals deviate — keeping our ranking quality high for users across all markets.
- Collaborate Across Disciplines: Work as a technical partner with Backend Engineers, Data Engineers, and Product Managers to deliver end-to-end improvements.
- Translate complex ML trade-offs into clear narratives for non-technical stakeholders, and contribute to shaping the team's roadmap.
- Raise the Bar: Mentor junior and mid data scientists, drive best practices in experimentation rigor and code quality, and actively contribute to a culture of learning — especially around emerging agentic development workflows.
Requirements
- Master's degree (or Bachelor's with 6+ years of work experience) in Computer Science, Mathematics, Physics, or a related quantitative field.
- 4+ years of industry experience as a Data Scientist or Machine Learning Engineer applying ML in high-traffic production environments.
- Deep Learning Ranking & LTR: Hands-on, production-proven experience implementing modern deep learning ranking architectures — DCN-V2, MMoE, Two-Tower — with strong command of multi-task learning, cross-feature interactions, and embedding optimization at scale.
- Solid foundation in LTR methods (pointwise, pairwise, listwise), offline evaluation metrics (NDCG, MRR), and the challenges of bridging offline metrics to online A/B outcomes.
- Agentic & AI-Assisted Development: Proficiency with LLM coding agents and agentic CLI tools (e.g., Claude Code, Gemini) as first-class tools in your development workflow — not just for boilerplate, but for autonomous experiment generation, architecture prototyping, and rapid iteration.
- Familiarity with the auto-research paradigm and comfort reviewing large volumes of autonomously generated experiment results.
- Technical Depth & MLOps: Expert in Python and its ML ecosystem (PyTorch, TensorFlow, scikit-learn).
- Comfortable with SQL (dbt), cloud environments (GCP or AWS), and big-data processing at scale (PySpark, Scala).
- Strong command of the full ML lifecycle — feature stores, vector databases, model versioning, A/B experimentation frameworks, and deployment to high-throughput, low-latency serving infrastructure.
- Hands-on experience with MLOps practices: CI/CD for ML pipelines, Metaflow, experiment tracking, model registry, and production monitoring covering drift detection, latency tracking, and data quality alerting.
- Problem-Solving & Ownership: Comfortable navigating ambiguity and translating broad business objectives into well-scoped data science projects.
- You hold yourself accountable for outcomes, not just outputs.
- Collaborative Spirit: Demonstrated ability to work effectively in cross-functional, globally distributed teams.
- Strong communication skills to articulate model behavior, experiment results, and trade-offs to both technical and non-technical audiences.
Conditions
- Make the most of our hybrid working model and join the team for face-to-face connection and collaboration in our beautiful Berlin campus 2 days a week.
- We offer 27 days holiday with an extra day on 2nd and 3rd year of service.
- We will support you in developing yourself and your career growth opportunities: 1.000 € Educational Budget, Language Courses, Parental Support and access to the Udemy Business platform to explore.