<p><strong></strong></p> <p><strong><span data-contrast="auto">About BEES</span></strong></p> <p><span data-contrast="auto">Join us to build the future of B2B commerce!</span><br><span data-contrast="auto">BEES is AB InBev’s B2B platform. Through our ecosystem, merchants and retailers across 29 countries can stock their businesses quickly, easily, and securely.</span> <br><span data-contrast="auto">At BEES, we dream big, lead with purpose, and develop technology that transforms the way retailers and sellers grow.</span> <span data-ccp-props="{"201341983":0,"335559739":0,"335559740":240}"><br></span><span data-contrast="auto">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!</span> <span data-ccp-props="{"134233117":true,"134233118":true,"201341983":0,"335559739":0,"335559740":240}"> </span></p> <p><strong></strong></p> <p><strong>About the Role:</strong></p> <p>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.</p> <p><strong>What you'll do:</strong></p> <ul> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>Utilize Python (FastAPI, PySpark) for data manipulation, embedding pipelines, and modeling.</li> <li>Implement automation and evaluation workflows: offline eval, A/B testing, monitoring of retrieval and generation quality to streamline iteration and enhance productivity.</li> <li>Collaborate closely with cross-functional teams, including data engineers, ML/platform engineers, business stakeholders, and product managers, to understand GenAI and search requirements and deliver high-quality, production-grade solutions. Embrace an agile development approach to iterate quickly and efficiently, and mentor other data scientists on GenAI and retrieval best practices.</li> </ul> <p><strong>What you'll need:</strong></p> <ul> <li>Bachelor's degree in computer science, engineering, mathematics, or another quantitative field. A master's degree or PhD is a plus.</li> <li>Relevant experience in data science or applied machine learning, with strong technical skills and a deep understanding of frontier GenAI techniques: LLMs, multimodal (text-to-image) generation, prompt engineering, and RAG architectures.</li> <li>Hands-on experience with search and retrieval systems: embedding models, vector/similarity search, hybrid (semantic + keyword) search, and re-ranking, along with a strong grasp of retrieval performance: latency, throughput, and recall/precision trade-offs, and how to evaluate and tune them in production.</li> <li>Experience with LLM/embedding APIs (OpenAI, Azure OpenAI, or similar). Experience implementing and deploying generative and retrieval models using these frameworks.</li> <li>Experience working with cloud platforms such as Azure (Azure OpenAI/Foundry, Blob Storage, Event Hub), Databricks, and Spark for big data processing, model serving, and scalable GenAI system design.</li> <li>Proficiency in Python/PySpark for data manipulation, analysis, and modeling tasks. Strong knowledge of API frameworks (e.g., FastAPI) for productionizing models as services.</li> <li>Good knowledge of CI/CD tools like GitHub for version control and collaboration, and of testing/typing tooling (pytest, mypy, linting) for shipping reliable, production-grade GenAI services. Understanding of workflows and automation tools, including async job orchestration and evaluation pipelines, to streamline processes and enhance efficiency.</li> </ul> <p><strong>What We Offer:</strong></p> <ul> <li>Performance based bonus*</li> <li>Attendance Bonus* </li> <li>Private pension plan</li> <li>Meal Allowance</li> <li>Casual office and dress code</li> <li>Days off*</li> <li>Health, dental, and life insurance</li> <li>Medicines discounts</li> <li>WellHub partnership</li> <li>Ch