Sr. Lead Data Product Manager

Overall Purpose

The Sr. Lead Data Product Manager serves as the dedicated Data Product Manager for the Data Science organization, partnering closely with Data Scientists, Data Engineers, and ML Ops teams to enable adoption of the enterprise data and machine learning platform (Domino Data Lab). The role focuses on developing a vision and requirements for scaling second- and third-party data with an emphasis on quality, usability, and risk management, translating stakeholder needs into actionable data, reporting, and analytics requirements.

• Articulate vision, roadmap, and technical requirements for data and analytics capabilities, processes, and tools to enable prioritized use cases across the enterprise.
• Act as the primary liaison between Data Science and ML Ops teams, translating business and technical requirements into actionable platform capabilities, migration plans, and backlog priorities.
• Lead requirements gathering, prioritization, and execution planning for the migration of data science workloads, models, and analytical processes to the enterprise ML Ops workbench.
• Manage and prioritize a balanced product backlog that includes new features, technical debt, operational risks, platform enhancements, and defect remediation, ensuring alignment with Product-Oriented Thinking and Delivery (POTD) principles and business outcomes.
• Partner with engineering and platform teams to identify, troubleshoot, and resolve data availability, data quality, and platform integration issues impacting data science and analytics use cases.
• Define and monitor adoption, performance, and value realization metrics associated with data science platform modernization and migration efforts.
• Advocate for data management best practices, governance requirements, and operational excellence to ensure trusted, scalable, and compliant use of enterprise data assets.
• Develop and maintain the data product roadmap; scope and prioritize activities based on the CDO, business, and user impact, defining product enhancements for both short-term and long-term.
• Gain a deep understanding of end-user needs and experience (internal data consumers), identify and fill product gaps, and generate new ideas that grow adoption and improve user experience.
• Define requirements for the Data Technology team on data processing and storage to facilitate a variety of analytics use cases such as effective analysis and reporting, data science exploration and model development, and data quality and anomaly detection.
• Set and achieve success metrics to meaningfully improve business results, focusing on what success looks like for internal data consumers and how accessible data and insights could help them deliver enterprise value.
• Act as a product evangelist to build awareness, understanding, adoption, and buy-in.
• Support the company's commitment to risk management and protect the integrity and confidentiality of systems and data, overseeing the integration of strong data governance, risk, and security controls.

• Education and/or experience typically obtained through completion of a bachelor’s degree in STEM or related field.
• 9+ years of experience in product management, data management, or consulting with a proven record of high performance, preferably with experience building data and analytics products.
• 5+ years of experience with direct hands‑on responsibility working with data or data teams (data engineers, data scientists, software developers, data analysts, etc.), business stakeholders, and end users.
• Strong business intuition and the technical ability to understand, design, and explain complex product and data strategies to both business and technical audiences.
• Proficiency with software development methodologies such as Agile and experience working with Scrum teams and Agile tools such as Jira.
• Strong project and stakeholder management with the ability to work effectively with cross‑functional teams with diverse skill sets across all levels of the organization.
• Excellent oral and written communication skills.
• Hands‑on experience writing and tuning SQL.
• Comfort with product analytics and data visualization tools (e.g.,…