Product Manager, AI Platform

About the role

You enjoy shaping the future of product innovation as a hands-on leader, driving measurable value for customers, guiding successful launches, and exceeding expectations. Join a dynamic Corporate & Investment Banking team to build and scale a production entity data platform that enables trusted decisions across front-office workflows, risk and controls, and AI-driven applications.

Responsibilities

• Develops a product strategy and product vision that delivers customer value by establishing a single, trusted, global universe of organizations and an arbitrated “golden profile” that downstream teams and platforms can rely on in production.
• Manages discovery efforts and market research to uncover customer solutions and integrate them into the product roadmap, including partnering with front-office, operations, and control stakeholders to define measurable outcomes for match quality, duplicate reduction, profile completeness, and adoption.
• Owes, maintains, and develops a product backlog that enables development to support the overall strategic roadmap and value proposition, translating business needs into clear, testable requirements for entity resolution, attribute arbitration, challenge-and-override workflows, and data onboarding patterns.
• Balances deterministic rules with machine learning-assisted matching, ensuring resolution decisions are explainable, traceable, and auditable for downstream reliance.
• Owns the arbitration and “golden record” capabilities that select best attribute values using configurable logic (for example, consensus and recency), including workflows that allow expert challenge, override, and safe propagation of corrections with full provenance.
• Defines a third-party data onboarding strategy and operating model, prioritizing integrations based on business value and readiness, setting quality and documentation standards, and establishing scalable onboarding patterns that prevent uncontrolled schema sprawl.
• Delivers diagnostic and operational tooling that enables users and operators to understand why entities matched or did not match, how attribute selections were made, and where data quality issues are creating adverse outcomes.
• Introduces AI- and agent-assisted processing patterns to improve throughput and reduce manual intervention, while maintaining appropriate governance, human-in-the-loop controls, and objective evaluation of model performance over time.
• Pairs closely with engineering, applied machine learning, architecture, data governance, and business stakeholders to manage dependencies, ensure resiliency and stability, and drive executive-ready communication on progress, risks, and trade-offs.

Requirements

• 5+ years of experience or equivalent expertise in product management or a relevant domain area
• 3+ years of owning complex data products or platforms where correctness, scale, and adoption are equally critical
• Experience delivering operationally supported platforms, not presentations
• Strong technical fluency across data platform fundamentals, including entity modeling, mastering and arbitration patterns, metadata and lineage, provenance, and data quality dimensions
• Ability to reason about algorithmic and operational trade-offs, including precision/recall, false positives/negatives, latency/throughput, and explainability versus automation, and to translate these into product decisions and success metrics
• Experience working with cross-functional teams across engineering, data engineering, applied machine learning, operations, and governance, with proven ability to influence in a matrixed environment
• Strong product operating discipline, including dependency management, release planning, clear requirements definition, and executive-level communication

Skills

• Strong technical fluency across data platform fundamentals, including entity modeling, mastering and arbitration patterns, metadata and lineage, provenance, and data quality dimensions
• Ability to reason about algorithmic and operational trade-offs, including precision/recall, false positives/negatives, latency/throughput, and explainability versus automation, and to translate these into product decisions and success metrics
• Experience working with cross-functional teams across engineering, data engineering, applied machine learning, operations, and governance, with proven ability to influence in a matrixed environment
• Strong product operating discipline, including dependency management, release planning, clear requirements definition, and executive-level communication

Benefits

Includes comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more.

Pay

Base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, awarded in recognition of individual achievements and contributions.

Schedule

Not specified