Job Description
We are on the lookout for a Senior/Principal Data Product Manager to join the Item Discovery Domain 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 Item Discovery domain within QC Merchandising space in the Commercial Tribe is at the forefront of enabling customers to seamlessly find relevant products in our Q-Commerce Platforms. Our mission is to build intelligent, adaptive, scalable, and fair ranking systems that maximize item discoverability, customer satisfaction, merchandising opportunities and business impact. The team leverages data, personalization, and dynamic ranking strategies to ensure that users efficiently find the right products while driving sustainable growth for all partners, regardless of their size.
We are seeking a strategic and data-driven Senior/Principal Data Product Manager to lead the development of data-centric systems in our QC Item Discovery domain, positioned within QC Merchandising value stream in the Commercial Tribe. You will work closely with cross-functional teams, engineering, data science, analytics, UX, platform and commercial teams to shape how customers navigate and engage with products in a high-impact, data-rich environment.
1. Drive the Strategy & Roadmap: Define the vision and strategic roadmap for data-driven systems across various real estates within the Item Discovery domain, ensuring alignment with overall business objectives, merchandising opportunities and customer experience goals.
2. Lead Data Product Development: Spearhead the development of dynamic ranking systems, ML services, and scoring mechanisms that surface relevant products in real-time, based on user context, product attributes, and business objectives.
3. Own Metrics & Performance: Establish and monitor success metrics related to ranking accuracy, engagement, and conversion, ensuring data-driven decision-making and continuous optimization.
4. Partner with Engineering & Data Science: Collaborate closely with engineering and data science to implement ranking algorithms, evaluate ML models, and leverage data pipelines that support adaptive data strategies.
5. Experimentation & Testing: Drive A/B testing and experimentation to validate hypotheses, optimize model performance, and assess the impact of new discovery strategies.
6. Stakeholder Alignment: Partner with stakeholders across Platforms, Data, UX, and more to align Merchandising Strategies.
Qualifications
7. Product & Domain Expertise: 5+ years in product management with a focus on data products, ranking systems, search, or relevance algorithms—ideally within Q-Commerce, e-commerce, or marketplace environments.
8. Data-Driven Impact: Proven ability to build ML-based products that leverage large datasets and rigorous experimentation (A/B testing) to optimize discoverability, engagement, and business KPIs.
9. Technical Fluency: Strong understanding of data pipelines, scoring logic, and ML models; comfortable working closely with engineers, data scientists, and backend teams.
10. 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.
11. User-Centered Thinking: Ability to translate customer behavior and insights into actionable product strategies that improve discovery and relevance.
12. Influence & Communication: Track record of leading cross-functional teams, aligning stakeholders, and clearly communicating complex ideas across technical and non-technical audiences
Nice to have
13. Experience in Q-Commerce, e-commerce retail, or logistics.
14. Experience with ranking, personalization, or search algorithms.
15. Familiarity with real-time data processing, dynamic rankers, and predictive modeling.
16. Bachelor’s degree in Data Science, Computer Science, or a related field, or equivalent practical experience.