Data Product Manager – Machine Learning Platform

Job not on LinkedIn

🕒 July 15

🇧🇷 Brazil – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 28%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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AB InBev

WebsiteLinkedInAll Job Openings

10,000+ employees

🏨 Hospitality

📣 Marketing

✈️ Travel

Hospitality • Marketing • Travel

AB InBev is a global brewing company known for its extensive portfolio of over 500 beer brands, including iconic global and local names. Committed to sustainability and responsible drinking, the company has a heritage spanning more than 600 years. AB InBev continually seeks innovation in brewing and serves to celebrate beer as a beverage of moderation.

📋 Description

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

🎯 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 • 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

• 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