Data Product Manager

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
• 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) 

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 requirements, user stories, or acceptance criteria that engineering teams ca