Senior Product Manager - Data

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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