Data Product Manager, Dynamic Assortment - (Quick Commerce)

Job Overview

We are seeking a Product Manager to join the Dynamic Assortment tribe within the Quick Commerce team.

You will own and deliver data products that power our intelligent digital shelf, working closely with data science, engineering, and cross‑domain stakeholders.

Responsibilities

• Drive product vision and roadmap; translate the team's vision into a clear, actionable product roadmap, prioritizing features based on business impact.
• Lead data model development; partner with data scientists and engineers to develop, refine, and deploy machine learning models for intelligent product curation, personalization, and substitution.
• Own experimentation & optimization; embed systematic experimentation into the product workflow, design, run and analyze A/B tests to validate hypotheses and improve algorithm performance and business KPIs.
• Deliver on key initiatives; lead execution of strategic projects such as experimentation with customer‑facing Category Tree structure, Category Recommendation engine, and models for automated merchandising and cross‑listing.
• Cross‑functional stakeholder alignment; collaborate with Merchandising, Catalog, Data Analytics, Platforms Product, and commercial teams to align strategies and ensure data products meet business needs.
• Own metrics & performance; establish and monitor success metrics and KPIs related to model performance, customer satisfaction and business impact, guiding product decisions and iterations.

Qualifications

• Product & domain expertise: 3–5 years in product management with a strong focus on highly technical data products in e‑commerce, marketplaces, or Q‑Commerce; experience with personalization or recommendation engines is a strong plus.
• Technical acumen & leadership: Proven ability to build products that leverage large datasets, machine learning models, and rigorous experimentation; comfortable leading technical conversations with data science and engineering teams about system architecture and data pipelines.
• Technical fluency: Strong understanding of machine learning concepts, data modeling, and experimentation frameworks; able to discuss trade‑offs in model implementations and guide scalable solutions.
• Analytical excellence: Skilled in SQL and data notebooks, able to derive insights, define success metrics, and iterate rapidly in ambiguous settings.
• Influence & communication: Track record of leading cross‑functional teams, aligning diverse stakeholders, and communicating complex ideas to technical and non‑technical audiences.
• Customer‑centric thinking: Ability to translate customer needs and business opportunities into data‑driven product features that enhance discovery, personalization and mission fulfillment.
• Nice to Have
• Experience with large‑scale personalization or recommendation systems.
• Familiarity with the lifecycle of machine learning models.
• Bachelor’s degree in Data Science, Computer Science, or related technical field, or equivalent practical experience.
• Experience with merchandising or retail analytics.
• Proven ability to break down complex initiatives into actionable plans and drive execution to completion.
• Track record of building consensus among diverse stakeholders and driving aligned decision‑making.

Benefits

• Hybrid working model: face‑to‑face collaboration at the Berlin campus 2 days a week.
• 27 days holiday with an extra day in the 2nd and 3rd year of service.
• Career development support: €1,000 educational budget, language courses, parental support and access to Udemy Business.
• Health and wellness: health check‑ups, meditation and gym.
• Employee share purchase plan, sabbatical bank, public transportation ticket discount, life & accident insurance, corporate pension plan.
• Digital meal vouchers, food vouchers and corporate discounts.
• Equal Opportunity Statement

We believe diversity and inclusion are key to creating an exciting product and an amazing customer and employee experience.

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.

Severely disabled applicants with equal qualifications will be given preferential consideration.

If you need accommodations or specific accessibility support for your interview experience, let us know.

You are welcome to share your pronouns (he/she/they) so we can address you respectfully.

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