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Delivery Hero

Senior Data Scientist - (Global Search, Consumer)

Hybrid · Berlin, Germany

This hybrid role accepts applicants from: Germany.

Apply on the employer's siteOriginal listingJSON

PeopleSeek Talent does not submit applications. The button opens the employer's own page; this listing was verified 11 hours ago.

PayNot listed
Work arrangementHybrid
Who can applyPeople in Germany
LevelSenior
TypeFull time
Occupationdata scientist
Languageen
Posted27 October 2026
Last verified11 hours ago
StatusActive

About the role

Job Description

Deep analysis. Smarter systems. Real users. As a Senior Data Scientist (Global Search, Consumer), you’ll own the deep learning ranking models behind the search services that power every Delivery Hero platform. You’ll also lead our move to agentic ML research, using LLM coding agents to run hundreds of overnight experiments and turn the strongest signals into ranking gains in production.

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.

At Delivery Hero, you’ll shape the global consumer experience for millions of users. Your work will have a direct impact on how we attract and retain customers, creating data-driven, personalized interactions that keep them coming back. As part of our Consumer Team, you’ll enhance customer satisfaction and drive global growth and profitability through innovative projects.

  • Own the ranking stack end to end: design, build, and productionize deep neural ranking models (DCN-V2, MMoE, Two-Tower) for high-throughput, low-latency serving, from offline experimentation and evaluation through monitoring and iterative improvement.
  • Lead agentic ML research: use LLM coding agents (e.g., Claude Code, Gemini) to autonomously iterate on feature engineering and Learning to Rank (LTR) architectures, design overnight experiment pipelines inspired by the auto-research paradigm, and review the outputs of hundreds of runs to identify signals quickly.
  • Improve ranker quality through feature engineering and model architecture work, applying your expertise in ranking signals, feature stores, and embedding-based retrieval, and turning the latest research into production-grade systems.
  • Keep production models healthy by owning monitoring for model drift, latency degradation, and feature pipeline integrity, and acting quickly when signals deviate in any market.
  • Partner with Backend Engineers, Data Engineers, and Product Managers to deliver end-to-end improvements, explain ML trade-offs clearly to non-technical stakeholders, and help shape the team’s roadmap.
  • Mentor junior and mid-level data scientists and set the standard for experimentation rigor, code quality, and agentic development workflows.

Qualifications

  • A Master’s degree in Computer Science, Mathematics, Physics, or a related quantitative field (or a Bachelor’s with 6+ years of work experience), plus 4+ years as a Data Scientist or Machine Learning Engineer applying ML in high-traffic production environments.
  • Production-proven experience with deep learning ranking architectures (DCN-V2, MMoE, Two-Tower), including multi-task learning, cross-feature interactions, and embedding optimization at scale, plus a solid foundation in LTR methods (pointwise, pairwise, listwise), offline metrics (NDCG, MRR), and the challenges of bridging offline metrics to online A/B outcomes.
  • Proficiency with LLM coding agents and agentic CLI tools (e.g., Claude Code, Gemini) as first-class development tools for autonomous experiment generation, architecture prototyping, and rapid iteration, along with familiarity with the auto-research paradigm and comfort reviewing large volumes of generated experiment results.
  • Expert Python skills across the ML ecosystem (PyTorch, TensorFlow, scikit-learn), working knowledge of SQL (dbt), GCP or AWS, and big-data processing (PySpark, Scala), and hands-on MLOps across the full lifecycle: feature stores, vector databases, model versioning and registry, Metaflow, CI/CD for ML pipelines, experiment tracking, A/B frameworks, low-latency deployment, and production monitoring for drift, latency, and data quality.
  • Comfort turning broad business objectives into well-scoped data science projects, with accountability for outcomes, not just outputs.
  • Experience working in cross-functional, globally distributed teams, and the ability to explain model behavior, experiment results, and trade-offs to technical and non-technical audiences.

Additional Information

Ensuring you and all our Heroes are looked after, happy, and healthy is always on the menu. Because if you’re in good shape, then we’re in good shape.

  • 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 a variety of online courses.

  • Get moving and release those wonderful, mind-boosting endorphins: Health Checkups, Meditation & Gym. 

  • Cash. Dough. Cheddar. Whatever you call it, we’ll help you with it: Employee Share Purchase Plan, Sabbatical Bank,  Public Transportation Ticket Discount, Life & Accident Insurance, Corporate Pension Plan

  • The power of getting together over some food is unrivaled. Here are a few ways to help you do that. All the yum: Digital Meal Vouchers and Food Vouchers. 

  • Wondering what relocating to Berlin is like? In this article, we’ve put together 10 things you should know about moving to Berlin  and how Delivery Hero can support you. You can also visit our relocation hub and check out more information about moving to Berlin.

  • Ready to prepare for your interview? Check out the list of the 5 most common interview questions and answers created in collaboration with our recruiters.

Heads up: exact benefits may look a little different for intern, working student, fixed-term, and part-time roles.

Ready to join our team? If you’re excited to grow, collaborate and be part of the world’s leading delivery platform, we’d love to hear from you. Apply today!

We believe diversity and inclusion are key to creating not only an exciting product, but also an amazing customer and employee experience. Fostering this starts with hiring - therefore we do not discriminate on the basis of racial identities, religious beliefs, color, national origin, gender identities or expressions, sexual orientations, age, marital or disability statuses, or any other aspect that makes you, you.

We encourage you to let us know if you need any accommodations or specific accessibility support to ensure a smooth interview experience—just let us know with an email to our Inclusion Officer at inclusion@deliveryhero.com.

You're welcome to share your pronouns (he/she/they) right from the start so we can address you respectfully from our first contact.

Qualifications

  • A Master’s degree in Computer Science, Mathematics, Physics, or a related quantitative field (or a Bachelor’s with 6+ years of work experience), plus 4+ years as a Data Scientist or Machine Learning Engineer applying ML in high-traffic production environments.
  • Production-proven experience with deep learning ranking architectures (DCN-V2, MMoE, Two-Tower), including multi-task learning, cross-feature interactions, and embedding optimization at scale, plus a solid foundation in LTR methods (pointwise, pairwise, listwise), offline metrics (NDCG, MRR), and the challenges of bridging offline metrics to online A/B outcomes.
  • Proficiency with LLM coding agents and agentic CLI tools (e.g., Claude Code, Gemini) as first-class development tools for autonomous experiment generation, architecture prototyping, and rapid iteration, along with familiarity with the auto-research paradigm and comfort reviewing large volumes of generated experiment results.
  • Expert Python skills across the ML ecosystem (PyTorch, TensorFlow, scikit-learn), working knowledge of SQL (dbt), GCP or AWS, and big-data processing (PySpark, Scala), and hands-on MLOps across the full lifecycle: feature stores, vector databases, model versioning and registry, Metaflow, CI/CD for ML pipelines, experiment tracking, A/B frameworks, low-latency deployment, and production monitoring for drift, latency, and data quality.
  • Comfort turning broad business objectives into well-scoped data science projects, with accountability for outcomes, not just outputs.
  • Experience working in cross-functional, globally distributed teams, and the ability to explain model behavior, experiment results, and trade-offs to technical and non-technical audiences.

Listed via Delivery Hero · first seen 27 October 2026 · last verified 28 October 2026

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