Data Product Manager | Machine Learning Platform

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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.

As a Data Product Manager | Machine Learning Platform, you will own the product direction for a critical set of platform capabilities supporting the end-to-end machine learning lifecycle, from data and feature readiness through model development, deployment, and observability.

You will work closely with data scientists, machine learning engineers, data engineers, and platform teams, driving the evolution of a platform that enables safe, scalable, and consistent delivery of machine learning solutions.

What you'll do:

• Define and Advance ML Platform Strategy

• Set the vision and roadmap for your platform domain, prioritizing self-service, reliability, and reusability across the ML lifecycle.

• Define and evolve standards, contracts, and shared tooling that enable scalable adoption of platform capabilities.

• Drive Adoption and Platform Delivery

• Partner closely with data science, ML engineering, and data engineering teams to identify workflow bottlenecks and platform gaps.

• Deliver capabilities such as templates, CLIs, and standardized workflows, enabling users to execute repeatable tasks independently within clear guardrails.

• Advance key platform capabilities, including: Feature governance; Training-serving parity; Model release and promotion standards; Deployment patterns (batch and real-time); Observability and monitoring.

• Enable Scalable Machine Learning Operations

• Improve and standardize how models move from development to production, reducing manual effort and increasing reliability.

• Ensure platform capabilities provide clear ownership, consistent practices, and strong operational visibility.

• Drive adoption of shared platform components across multiple data science teams and markets.

What you'll need:

• Bachelor’s degree in Engineering, Computer Science, Mathematics, Statistics, or a related technical field — or equivalent practical experience

• Relevant years of professional experience with a strong technical foundation

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Hands-on experience as a data scientist, machine learning engineer, or software engineer is strongly preferred

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Experience working on complex, cross-functional technical problems involving data and platform systems

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Fluency in English (required)

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Portuguese or Spanish is a plus

• Strong written and verbal communication skills

• Comfortable discussing technical architecture, workflows, and trade-offs in depth;

• Advanced communication skills in English (written and spoken);

• Python (required): ability to read, understand, and reason about pipeline and platform code; discuss implementation trade-offs with engineers

• SQL (required): ability to run independent investigations to validate data, features, and metrics;

• Strong fluency in Machine Learning lifecycle and MLOps topics (e.g. Model training, evaluation, and experimentation; Monitoring, drift detection, and production operations; CI/CD for ML workflows; etc)

Nice to Have:

• Experience with Databricks and Azure.

• Prior product management experience on developer, data, or ML platform products.

• Familiarity with product and project management tools (Jira, Confluence).

• Experience defining and prioritizing a roadmap, backlog, or release plan.

• Comfort writing clear…