Data Scientist
Posted 28 Sep 2026
Advertised as “Senior Data Scientist - Generative AI ”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- People management
No salary listed for this role.
Field of studyComputer Science, Engineering, Mathematics (or related)
Study levelBachelor's
Study levelMaster's or higher
About us
AB InBev is the leading global brewer and one of the world’s top 5 consumer product companies. With over 500 beer brands, we’re number one or two in many of the world’s top beer markets, including North America, Latin America, Europe, Asia, and Africa.
About AB InBev Growth Group
Created in 2022, the Growth Group unifies our business-to-business (B2B), direct-to-consumer (DTC), Sales & Distribution, and Marketing teams. By bringing together global tech and commercial functions, the Growth Group allows us to fully leverage data and drive digital transformation and organic growth for AB InBev around the world.
In addition to supporting well known global beer brands like Corona, Budweiser and Michelob Ultra, the Growth Group is home to a robust suite of digital products including our B2B digital commerce platform BEES, on-demand delivery services Ze Delivery and TaDa Delivery, and table top beer keg PerfectDraft.
We are an exceptional team, focused on understanding and supporting consumer and customer needs, harnessing new technology, and scaling growth opportunities.
About the Role:
We're hiring a Senior Data Scientist to help lead our GenAI platform — the systems behind prompt-driven solutions and the retrieval/RAG pipelines that ground them in real product and brand data. You'll own both the generative side (LLM and diffusion-based creative pipelines) and the search side (embeddings, vector retrieval, RAG performance) of a production system serving live creative-generation traffic, and help set technical direction for how the team builds and evaluates GenAI and search systems going forward.
What you'll do:
- Design, build, and productionize GenAI systems end to end: from prompt engineering to the RAG architectures (retrieval, ranking, and generation) that ground them in accurate, brand-safe outputs.
- Own the search and retrieval layer for GenAI applications: embedding models, vector similarity search, and re-ranking strategies, continually evaluating retrieval quality (precision/recall, relevance) and latency/performance, and iterating on chunking, indexing, and hybrid search techniques.
- Stay up to date with the latest advancements in LLMs, multimodal generation, and retrieval-augmented generation. Apply frontier techniques (fine-tuning, few-shot/in-context learning, agentic and multi-step RAG) to solve complex creative and search problems, driving innovation across the team's GenAI roadmap.
- Utilize cloud and orchestration platforms (Azure, Azure OpenAI/Foundry, Databricks, Spark) to process large-scale datasets and serve GenAI and RAG systems efficiently and at production scale through async APIs and background job pipelines.