Product Manager – Customer Data

Role Overview

You’ll be the Product Manager – Customer Data helping Rhythm leverage customer data as a core business capability. This is a cross-functional role spanning Product, Data Engineering, and Data Science, focused on modeling, activating, and using customer data across Sales, Marketing, Operations, and Commercial teams.

Responsibilities

• Define and manage product requirements for customer data initiatives, translating business goals into PRDs/specifications, workflows, and implementation plans.
• Own the roadmap for customer data, balancing short-term outcomes with long-term strategic data capabilities.
• Partner with Sales, Marketing, Operations, and Commercial teams to identify data needs and turn them into actionable initiatives.
• Build data-powered capabilities such as:
  • Customer segmentation
  • Lifecycle insights
  • Churn/retention analysis
  • Value modeling
• Ensure initiatives drive measurable business impact (e.g., acquisition, retention, churn reduction, pricing optimization, cost-to-serve).
• Work hands-on with data using SQL and modern data platforms (e.g., Databricks) to validate and support delivery.
• Collaborate with Data Science across the model lifecycle (from ideation through production and monitoring).
• Partner with Data Engineering to ensure reliable, scalable systems and alignment around a single source of truth.
• Identify opportunities to turn internal data capabilities into customer-facing features and product improvements.

Requirements

• 3+ years in Product Management, Data Product, or analytics-heavy roles.
• Strong SQL skills and comfort working directly with data to generate insights and test hypotheses.
• Experience partnering with Data Engineering, Data Science, and/or Software Engineering to deliver data products.
• Strong stakeholder management and ability to align teams and drive decisions across competing priorities.
• Demonstrated ability delivering measurable business impact through data-driven initiatives.
• Ability to operate in fast-paced, ambiguous environments; define structure and processes.
• Solid understanding of the product development lifecycle (e.g., roadmap planning, PRDs, delivery cycles).
• Strong communication across strategic, technical, and execution layers.

Nice to Have

• Experience with Databricks.
• Exposure to customer lifecycle analytics (CLV, churn, segmentation, retention).
• Experience building data-driven product features or personalization systems.
• Familiarity with experimentation, modeling, or data science workflows.