Data Scientist - Quick Commerce at Delivery Hero

Berlin, , Germany -

Full Time

Start Date

Immediate

Expiry Date

10 Feb, 26

Salary

0.0

Posted On

12 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Machine Learning, Natural Language Processing, Classification, Recommendation Systems, Large Language Models, Python, Statistical Analysis, Model Validation, A/B Testing, Cross-Functional Collaboration, Cloud Platforms, Content Generation, AI Systems, Catalog Automation, Deep Learning

Industry

technology;Information and Internet

Description

Company 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 over 70+ 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. Job Description We are on the lookout for a Data Scientist to join our Quick Commerce team on our journey to always deliver amazing experiences. We are redefining how customers experience quick commerce. You’ll help build technology that scales our non-food offerings, reaching new market segments and driving revenue growth. By innovating within our Quick Commerce Team, you’ll make Delivery Hero the go-to platform for a broad range of products, helping us grow faster and deliver more value to customers around the world. At Content & Assortment, we are building the AI engine that powers our global product catalog. We want every product to be perfectly described, categorized, and presented to our customers. Our mission is to automate and optimize the entire lifecycle of our product catalog—from creation and enrichment to categorization and curation—using advanced Machine Learning. As a Data Scientist, you will work side-by-side with Data Scientists, Machine Learning Engineers, and Software Engineers to design, build, and deploy ML models that automate content creation and power a smarter product assortment. This is a role for someone who has a strong background in applied ML, particularly in NLP, classification, or recommendation systems, and is hungry to solve complex catalog problems at scale. You will drive hypotheses, validate model accuracy, and bring ML-powered services for attribute enrichment, product categorization, and assortment curation into production with real business impact. If you’re a strong coder with a problem-solving mentality and thrive in high-scale applied ML problems, we’d love to talk. Your mission: Design, experiment, and deploy AI/ML models/Agents to automate the enrichment and moderation of product attributes at scale. Drive experimentation: formulate hypotheses, validate model performance , analyze business impact, and turn insights into production-ready models. Develop and provide ML models to intelligently curate the product assortment, including attribute extraction, product categorization, category tree recommendations, and the curation of high-impact featured categories. Keep yourself up to date and apply state-of-the-art models (e.g., in NLP, deep learning, classification, LLMs/AI agents) to see what truly works best for our catalog automation. Collaborate closely with MLEs and Engineers to ensure solutions are scalable, reliable, and fast in production. Contribute to the long-term vision of catalog automation and AI-driven assortment, while delivering short-term wins that unlock measurable business impact. Qualifications What you need to be successful: 3+ years of industry experience applying Data Science and Machine Learning in production, ideally in large-scale consumer-facing applications. Deep expertise in building and deploying ML models, particularly in Natural Language Processing (NLP), classification, recommendation systems and LLMs or agentic frameworks. Strong statistical foundations and ability to distinguish signal from noise in large-scale experimentation and model validation. Excellent engineering skills: write clean, maintainable Python code; experience bringing ML models into production with best practices for observability, monitoring, and performance. Experience with large-scale ML model validation, experimentation, and A/B testing. Proven ability to work cross-functionally with engineers and product stakeholders, translating business problems into data science solutions. Nice to have: PhD in AI, Machine Learning, or a related field. Experience scaling catalog automation or content generation systems across multiple markets and heterogeneous platforms. Experience in designing or building Agentic AI systems. Experience in fine tuning Large Language Models. Familiarity with cloud platforms (GCP preferred). Contributions to the data science community (conference talks, papers, OSS). Additional Information 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 include it in your application. You're welcome to share your pronouns (he/she/they) right from the start so we can address you respectfully from our first contact.

Responsibilities

As a Data Scientist, you will design, experiment, and deploy AI/ML models to automate the enrichment and moderation of product attributes at scale. You will collaborate with engineers to ensure solutions are scalable and reliable while contributing to the long-term vision of catalog automation.