Product Manager I, Data Platform

Memorial Sloan Kettering Cancer Center•New York, NY

1d•Hybrid

About The Position

Memorial Sloan Kettering Cancer Center is building modern data products and platforms to enable better decisions, innovation, and operational excellence. The Product Manager I on the Data Platform team partners across technical and business teams to develop data products supporting analytics, AI, applications, and enterprise reporting. The role works with users, business stakeholders, engineers, architects, and analytics teams to understand needs and turn them into product requirements, user stories, and acceptance criteria. It manages and refines the backlog, helps prioritize and sequence work, and supports development and delivery of data products and platform capabilities. The position also uses product usage, adoption, performance, and other metrics to inform decisions; contributes to roadmaps, priorities, and success metrics; and supports scalable, secure, and reliable data solutions. The Product Manager I gathers user and stakeholder feedback, identifies ways to improve products and user experience, and coordinates launches, communications, documentation, training, and adoption. The role is expected to develop expertise in modern data platforms, cloud technologies, analytics, governance, AI, and emerging technologies to identify opportunities for MSK’s data ecosystem. This is a hybrid position at 633 Third Ave, with in-office work on Wednesdays and Thursdays. The schedule is 9 a.m. to 5 p.m., Monday through Friday, for 37.5 hours per week. The position reports to the Associate Director, Product Management. The stated pay range is $92,700–$148,400.

Requirements

Bachelor’s degree or equivalent combination of education and experience.
2–4 years of experience in product management, business analysis, technology, data analytics, consulting, or a related field.
Experience working with data platforms, data products, analytics, or data-driven technology initiatives.
Experience gathering, documenting, and refining business or product requirements and working with technical and non-technical stakeholders.
Experience supporting cross-functional initiatives within a complex enterprise environment.
Strong communication skills to convey requirements, priorities, and product decisions to technical and non-technical audiences.
Highly organized and detail-oriented, with the ability to triage requests, manage competing priorities, and follow work through to completion.
Data-driven problem-solving skills, using analytics, metrics, and structured thinking to evaluate opportunities and inform decisions.
Strong collaboration skills and the ability to build relationships across engineering, architecture, analytics, operations, and business teams.
Curiosity and adaptability, with an interest in data platforms, cloud technologies, analytics, AI, and continued growth in product management and technology.

Nice To Haves

Familiarity with analytics and visualization tools such as Tableau or Power BI.
Familiarity with modern data platforms such as Databricks.
Familiarity with cloud environments such as AWS.

Responsibilities

Partner with users and business stakeholders to understand needs and translate them into clear product requirements, user stories, and acceptance criteria.
Collaborate with engineers, architects, and analytics teams to support data product and platform development.
Manage and refine the product backlog, triage requests, organize priorities, and help sequence work across the data platform.
Understand upstream and downstream dependencies to guide effective product delivery.
Analyze product usage, adoption, performance data, and other metrics to inform priorities and product decisions.
Help define product roadmaps, priorities, and success metrics for data platform capabilities.
Partner with engineering and architecture teams to deliver scalable, secure, and reliable data solutions.
Conduct user research, gather stakeholder feedback, and identify opportunities to improve data products and user experience.
Coordinate product launches, communications, documentation, training materials, and adoption activities.
Build expertise in modern data platforms, cloud technologies, analytics, governance, AI, and emerging technologies to identify future opportunities for MSK’s data ecosystem.