Data Product Manager - GenAI & Search
- Opened · 30d
- +0
- Closed · 30d
- −0
- All product
- 8,165open
About BEES
Join us to build the future of B2B commerce!
BEES is AB InBev’s B2B platform. Through our ecosystem, merchants and retailers across 30 countries can stock their businesses quickly, easily, and securely.
At BEES, we dream big, lead with purpose, and develop technology that transforms the way retailers and sellers grow.
Every line of code and every partnership is built in service of a single mission: to make commerce better for retailers and sellers around the world. Here, your work is not just important. It makes a difference!
About the role
We are looking for a Technical Product Manager (Data/AI) to join our Generative AI & Search team, playing a critical role at the intersection of business, data science, and software engineering. You will own the strategy, execution, and adoption of GenAI-powered experiences, intelligent agents, and semantic search products used across global markets.
This role requires strong product leadership, **a solid technical background to guide and challenge the engineering and data teams**, and the ability to translate complex AI/Search architectures into tangible business value for commercial and customer stakeholders.
What you'll do
Drive Strategic Alignment
- Partner with global commercial and product stakeholders across LATAM, US, and global markets to enable product understanding, drive adoption, and unlock new Generative AI and Search use cases.
- Act as a product evangelist—educating stakeholders on LLM/Search capabilities, trade-offs (latency, cost, accuracy/hallucinations), limitations, and product roadmap.
- Provide regular updates to internal stakeholders and leadership on product adoption, search relevance, agent performance, and business impact.
Plan & Prioritize
- Own quarterly product planning in close collaboration with AI/ML Engineers, Data Scientists, Data Engineers, and Software Engineers.
- Leverage your technical background to balance business value, model/prompt experimentation, search relevance tuning, and platform/infrastructure scalability when defining priorities.
- Continuously assess team capacity, technical debt, and delivery risk to ensure plans are realistic, achievable, and aligned with broader product and business strategy.
Execute & Deliver
- Partner closely with technical teams to execute the quarterly backlog and deliver high-impact GenAI and Search capabilities.
- Drive the end-to-end delivery of: Conversational AI & Agentic workflows, Semantic & Hybrid Search engines (RAG, vector search), Scalable and low-latency APIs, Evaluation frameworks (LLM-as-a-judge, offline/online evals), and observability dashboards.
- Define Epics, High-Level Requirements (HLRs), and technical specs, leading refinement sessions with a strong focus on: System architecture, User experience (UX), Search relevance & ranking, and LLM behavior, guardrails, and performance.
- Lead Agile ceremonies, including daily stand-ups, sprint planning, reviews, and retrospectives, acting as a technical sounding board for the squad.
- Track delivery and performance metrics, producing sprint summary reports with emphasis on: Team productivity, Search & AI quality metrics (e.g., NDCG, MRR, faithfulness, latency, token cost), and usage/adoption trends.
Engage Stakeholders
- Maintain strong relationships with technical, commercial, and senior leadership stakeholders.
- Present quarterly roadmaps, architectural trade-offs, and progress updates with clarity and confidence.
- Proactively manage expectations by negotiating scope, timelines, and technical trade-offs across internal teams and customers.
What you'll need
- Strong technical background (e.g., degree or prior hands-on experience in Computer Science, Software Engineering, Data Science, or Systems Architecture) with the ability to constructively guide technical discussions and architecture decisions with engineers and scientists.
- Proven experience managing data, Search, or AI/ML-driven products, with a strong grasp of Generative AI concepts (LLMs, RAG, Prompt Engineering, Agents, Embeddings) and Information Retrieval / Search (Lexical vs. Vector/Semantic Search, ranking, indexing).