Data Product Manager – Machine Learning Platform

AB InBev

Data Product Manager responsible for driving ML platform strategy and adoption at AB InBev. Collaborating with cross-functional teams to enhance machine learning solutions.

Posted 7/15/2026full-timeRemote • 🇧🇷 BrazilMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Machine Learning Operations (MLOps) and the Machine Learning lifecycle, with strong capabilities in Python and SQL for data validation and platform implementation. Effective in driving platform strategy and adoption through advanced communication and technical problem-solving skills.

Highest-signal resume keywords

Machine Learning Operations (MLOps)Python ProgrammingSQL ProficiencyTechnical Communication SkillsData Science Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning LifecycleModel TrainingModel EvaluationExperimentationMonitoringDrift DetectionProduction OperationsCI/CD for ML WorkflowsData ValidationTechnical Problem Solving

Soft Skills

Strong Written CommunicationStrong Verbal CommunicationCross-Functional Collaboration

Industry Keywords

Machine LearningData ScienceTechnical ArchitecturePlatform StrategyWorkflow Management

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Define and Advance ML Platform Strategy
Drive Adoption and Platform Delivery
Enable Scalable Machine Learning Operations

Requirements

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
Hands-on experience as a data scientist, machine learning engineer, or software engineer is strongly preferred
Experience working on complex, cross-functional technical problems involving data and platform systems
Fluency in English (required)
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)

Benefits

Comp & perks

Performance based bonus*
Attendance Bonus*
Private pension plan
Meal Allowance
Casual office and dress code
Days off*
Health, dental, and life insurance
Medicines discounts
WellHub partnership
Childcare subsidies
Discounts on Ambev products*
Clube Ben partnership
Scholarship*
School materials assurance
Language and training platforms
Transport allowance