Data Product Manager, Dynamic Assortment - (Quick Commerce)

Job Description

We are on the lookout for a Product Manager to join the Dynamic Assortment tribe within the Quick Commerce team on our journey to always deliver amazing experiences.

Be part of 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.

The Dynamic Assortment team operates as a specialized data team within the QC Assortment domain. Our mission is to maximize customer lifetime value and drive profitable growth by transforming our static product catalog into an adaptive and dynamic one. We develop and refine sophisticated data models that intelligently curate and personalize the product assortment, enhancing the customer experience and optimizing business outcomes. Our work is a critical enabler of the Quick Commerce strategy to make every product "easy to list, effortless to discover, and inspiring to convert."

We are seeking a strong Product Manager to own and deliver the data products that power our intelligent digital shelf. You will work closely with data science, engineering, and cross-domain stakeholders to translate our vision into a tangible product roadmap. You will be at the forefront of transforming our static catalog into an adaptive, personalized experience by leveraging unified demand intelligence, deploying advanced machine learning models, and embedding a culture of continuous experimentation and optimization.

This role requires a blend of strategic thinking, technical depth, and a passion for data. You will be responsible for the entire lifecycle of our data products, from ideation and model development to experimentation and performance monitoring, directly influencing key growth metrics such as conversion rates, average order value, and customer retention.

• Drive Product Vision & Roadmap: Translate the team's vision into a clear, actionable product roadmap. Define and prioritize features and initiatives based on business impact, customer value, and technical feasibility.
• 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 and rapid iteration into the product development workflow. Design, run, and analyze A/B tests to validate hypotheses and drive measurable improvements in algorithm performance and business KPIs.
• Deliver on Key Initiatives: Lead the execution of strategic projects, such as implementing experimentation with customer-facing Category Tree structure, driving the Category Recommendation engine, and developing models for automated merchandising and cross-listing.
• Cross-Functional Stakeholder Alignment: Partner with stakeholders across Merchandising, Catalog, Data Analytics, Platforms Product, and commercial teams to align on strategies and ensure our data products meet business needs. Articulate complex data concepts to non-technical audiences to build consensus and drive decision-making across our global platforms.
• Own Metrics & Performance: Establish and monitor success metrics and KPIs related to model performance, customer satisfaction, and business impact. Use data-driven insights to guide product decisions and iteration.

Qualifications

• Product & Domain Expertise: 3–5 years in product management with a strong focus on highly technical data products with a focus on data-centric products in e-commerce, marketplaces, or Q-Commerce. Experience with personalization or recommendation engines is a strong plus. You should demonstrate a proven ability to lead technical product initiatives from conception to launch.
• Technical Acumen & Leadership: Proven ability to build products that leverage large datasets, machine learning models, and rigorous experimentation (A/B testing). You should be comfortable driving technical conversations with data science and engineering teams about system architecture, model performance, and data pipelines.
• Technical Fluency: Strong understanding of machine learning concepts, data modeling, and experimentation frameworks. You should be able to discuss trade-offs in model implementations and guide the team toward scalable, effective solutions.
• Analytical Excellence: Skilled in SQL and data notebooks, with the ability to derive insights, define success metrics, and iterate rapidly in fast-paced, ambiguous settings. You can independently analyze data to identify issues and opportunities.
• Influence & Communication: A track record of leading cross-functional teams, aligning diverse stakeholders, and clearly communicating complex ideas across 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.