Sr. Lead Data Product Manager

Requirements

• Education and/or experience typically obtained through completion of a bachelor's degree in STEM or related field
• 9+ years' experience or related experience in product management, data management, or consulting w

Benefits

Additional Information

At Early Warning, we've powered and protected the U.S. financial system for over thirty years with cutting-edge solutions like Zelle®, Paze℠, and so much more. As a trusted name in payments, we partner with thousands of institutions to increase access to financial services and protect transactions for hundreds of millions of consumers and small businesses. Positions located in Scottsdale, San Francisco, Chicago, or New York follow a hybrid work model to allow for a more collaborative working environment. Candidates responding to this posting must independently possess the eligibility to work in the United States, for any employer, at the date of hire. This position is ineligible for employment Visa sponsorship. The Sr. Data Product Manager is a strategic role within the Certos Data & Analytics organization focused on enabling Data Science and Analytics teams through deep data management expertise. This leader drives the vision and requirements to scale and improve second- and third-party data, with an emphasis on quality, usability, and risk management. By translating stakeholder needs into actionable data, reporting, and analytics requirements, the role partners closely with data science, analytics, and technology teams to deliver high-quality, trusted data products that accelerate model development, insights generation, and compliant data use. This role will be critical in enabling our future state data science and ML Ops platform and support the requirements to build out capabilities. 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). his role will dive deeply into customer pain points, technology and security constraints, and market trends to develop a vision and requirements to scale second- and third-party data focusing on quality, usability, and risk management. They will translate stakeholder (internal/external) needs into technical data, reporting and analytics requirements, and partner across technology teams to facilitate the development of data products and capabilities. Essential Functions: Articulate vision, roadmap and technical requirements for data and analytics capabilities, processes and tools to enable prioritized use cases across the enterprise or business unit 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; scopes and prioritizes activities based on the CDO, business, and user impact which defines 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 our 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. Oversee the integration of strong data governance, risk and security controls.

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