Our DS/MLE team builds and operates data science and machine learning services consumed across the organization, Product Engineering, Customer Service, Finance, HR, and other business functions. We're looking for a Senior Product Manager who can translate diverse stakeholder needs into a coherent product strategy for these data/ML services, and own the roadmap that connects model capability to real business outcomes.

About the role:

• Set the product roadmap and strategy for DS/MLE services, aligned to the needs of multiple internal stakeholder teams with different levels of data/ML literacy
• Act as the primary interface between the DS/MLE team and consuming teams — translate business problems into product requirements, and translate technical constraints/trade-offs back into terms stakeholders can act on
• Lead exploratory investigations into service performance and adoption (accuracy, latency, usage, business impact) and turn findings into prioritized initiatives
• Collect and synthesize feedback from stakeholders to shape requirements, features, and prioritization of data/ML products
• Own the tracking record of shipping reliable, on-time products with measurable business impact across the services stakeholders depend on
• Guide, coach, and coordinate product managers/analysts in delivering multiple concurrent projects with cross-functional engineering, DS, and MLE teams
• Partner with DS/MLE engineering leadership on platform and infrastructure priorities that affect product delivery (e.g. deployment speed, service reliability)

About you:

• At least 4 years of experience as a product manager; experience with data, ML, or platform products strongly preferred
• Extremely strong communication skills — able to work fluently with both highly technical (DS/MLE engineers) and non-technical (CS, Finance, HR) stakeholders
• Strong intuition for stakeholder behavior and expectations, especially in reconciling competing priorities across business functions
• Strong command of fundamental product management concepts, practices, and procedures
• Excellent analytical and problem-solving skills — comfortable digging into data/model performance metrics to diagnose issues and identify optimization opportunities, not just qualitative feedback
• Working understanding of how ML models are built, deployed, and monitored in production — enough to have credible technical conversations with DS/MLE engineers, even without hands-on modeling experience
• Self-starter with strong project management skills to follow through on execution
• Good leadership skills, including the ability to manage, coach, and direct a team
• English is a must (spoken and written)

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