# Senior Data Scientist - (Global Search, Consumer) at Delivery Hero (talabat)

Delivery Hero (talabat) is hiring a Senior Data Scientist - (Global Search, Consumer) in Berlin, Germany. Level rates it Builds AI ●●●●; you can [apply on Delivery Hero (talabat)](https://jobsbylevel.com/go/ee22f926-fcee-42f0-bb72-a032b2609375?utm_medium=ai-agent).

AI Level 4, AI centrality 95 out of 100. Berlin, Germany.

## Details

- Company: [Delivery Hero (talabat)](https://jobsbylevel.com/companies/delivery-hero-talabat)
- AI level: AI Level 4 (score 95 out of 100)
- Location: Berlin, Germany
- Posted: October 27, 2026
- Apply: https://jobsbylevel.com/go/ee22f926-fcee-42f0-bb72-a032b2609375?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-data-scientist-global-search-consumer-at-delivery-hero-talabat-31af0f

## Prepare for this job

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## Description

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. 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. 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,

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