Staff Product Manager, Data & ML Platform

About the role

You will manage Faire's data and machine learning infrastructure as a core product, ensuring these systems are reliable, scalable, and easy for internal teams to use. By defining clear roadmaps and user requirements, you will enable engineers and scientists to build high-quality marketplace features like search and personalization. You will own the end-to-end lifecycle of ML products, translating ambiguous problems into structured solutions that balance technical constraints with business needs. You will act as the central interface between data platform teams and consumer teams, aligning diverse stakeholders on a shared vision for platform capabilities. You will define success through user outcomes such as model shipment velocity and support load reduction rather than just process metrics. You will improve core data reliability by establishing data contracts, ownership models, and service level agreements to create a robust foundation for experimentation. You will enhance metadata quality, lineage, and discovery to ensure teams can trace and trust data assets across the organization.

Key facts

What you'll do

• Lead the end-to-end lifecycle of ML products, from initial discovery with scientists to production deployment, including model registries and observability.
• Define success through user outcomes such as model shipment velocity and support load reduction rather than just process metrics.
• Improve core data reliability by establishing data contracts, ownership models, and service level agreements.
• Enhance metadata quality, lineage, and discovery to ensure teams can trace and trust data assets.
• Build internal data products like semantic layers and reusable dimension tables to improve developer experience.
• Manage a transparent platform roadmap, including intake processes, cost efficiency tracking, and adoption metrics.
• Partner with data and machine learning engineers to translate platform constraints into clear technical requirements and implementation plans.
• Conduct discovery sessions with internal users to surface pain points and define opportunities for platform improvements.
• Prioritize features based on impact, feasibility, and dependencies while maintaining a long-term view of platform scalability.
• Define and track key product metrics to measure adoption, performance, and user satisfaction across data and ML workflows.
• Collaborate with design and research partners to ensure platform tools are intuitive and meet the needs of both novice and expert users.
• Own the documentation strategy for platform components, ensuring that APIs, data models, and workflows are clearly communicated.
• Work closely with the data governance team to ensure platform practices align with company standards and regulatory requirements.
• Drive continuous improvement by analyzing feedback loops and iterating on platform capabilities based on real usage patterns.
• Act as a thought leader in data and ML product management, contributing to internal playbooks and best practices.

Requirements

• 8+ years of product management experience with a focus on complex technical products at a Staff or Lead level.
• Technical fluency in data and ML systems, including model training, deployment, feature pipelines, and evaluation.
• Proven ability to ship platform products for internal technical users.
• Experience working in marketplace or data-intensive environments where algorithms drive conversion and retention.
• Skill in influencing cross-functional teams and negotiating technical priorities without direct positional authority.
• Strong written and verbal communication skills for presenting complex technical concepts to diverse audiences.
• Demonstrated experience with agile development processes and cross-team collaboration in a fast-paced environment.
• Willingness to engage in hands-on problem-solving and occasional deep dives into technical details to validate assumptions.

Skills & tools

• Data platform architecture
• Machine learning production systems
• Feature pipelines
• Model registry and deployment
• Data lineage and metadata management
• Strategic roadmap development
• SQL and data querying
• Cloud infrastructure concepts
• Observability and monitoring tools
• Product analytics and experimentation frameworks

Practical notes

• Hybrid work policy requires office attendance 3 days per week, specifically Tuesdays, Thursdays, and one flex day.
• Employees may work remotely for up to 4 weeks per year.
• Compensation is determined by factors including experience, market demand, and location.
• Faire provides equal employment opportunities and offers reasonable accommodations for candidates with disabilities via their online request form.