Data Product Manager | Machine Learning Platform
💰 $150,000 – $220,000/yrMarket estimate · not provided by the employer
Job Description
About AB InBev
AB InBev is the leading global brewer and one of the world's top 5 consumer product companies. With over 500 beer brands, we hold number one or two positions in many of the world's top beer markets, including North America, Latin America, Europe, Asia, and Africa.
About the Growth Group
Created in 2022, the Growth Group unifies business-to-business (B2B), direct-to-consumer (DTC), Sales & Distribution, and Marketing teams. By integrating global tech and commercial functions, the Growth Group drives data-led digital transformation and organic growth worldwide. The division supports global brands including Corona, Budweiser, and Michelob Ultra, alongside digital products such as BEES (B2B commerce platform), Ze Delivery, TaDa Delivery, and PerfectDraft.
The Role
As a Data Product Manager for our Machine Learning Platform, you will own the product direction for critical platform capabilities supporting the end-to-end machine learning lifecycle—from data and feature readiness through model development, deployment, and observability. You will partner with data scientists, machine learning engineers, data engineers, and platform teams to drive the evolution of a platform enabling safe, scalable, and consistent delivery of machine learning solutions.
Key Responsibilities
- 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: feature governance, training-serving parity, model release and promotion standards, deployment patterns (batch and real-time), and observability and monitoring.
- Enable Scalable Machine Learning Operations
- Improve and standardize how models move from development to production, reducing manual effort and increasing reliability.
This is a remote position based in Brazil.