AB InBev - Data Product Manager
Requirements
• 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 • Hands-on experience as a data scientist, machine learning engineer, or software engineer is strongly preferred • data scientist, machine learning engineer, or software engineer • Experience working on complex, cross-functional technical problems involving data and platform systems • complex, cross-functional technical problems • 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) • 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 requirements, user stories, or acceptance criteria that engineering teams can execute against.
Responsibilities
• 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. • 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. • 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.
Benefits
• Performance based bonus* • Attendance Bonus* • Private pension plan • Casual office and dress code • Health, dental, and life insurance • Medicines discounts • WellHub partnership • Childcare subsidies • Discounts on Ambev products* • Clube Ben partnership • School materials assurance • Language and training platforms • Transport allowance • Equal Opportunity & Affirmative Action:
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