• 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.
• Owns, 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.
• Builds the framework and tracks the product’s key success metrics such as cost, feature and functionality, risk posture, and reliability, including precision/recall and false positive/negative rates, resolution throughput and cycle time, duplicate creation rates, golden profile correctness and completeness, and service-level targets for adjudication workflows.
• Leads delivery of entity resolution at scale across internal systems and third-party sources by balancing 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.
• Partners 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.

• 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.
• Demonstrated track record of shipping production products end-to-end, including roadmap ownership, backlog management, and measurable outcomes; 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.

• Demonstrated prior experience working in a highly matrixed, complex organization
• Experience in financial services, particularly Corporate & Investment Banking, including exposure to enterprise data controls and audit expectations.
• Prior experience with entity resolution or identity matching, deterministic rules frameworks, and machine learning-assisted matching or classification in high-volume environments.
• Experience designing explainability, auditability, and human-in-the-loop governance patterns for AI-enabled production workflows.
• Experience sourcing, normalizing, and integrating third-party data, including establishing scalable onboarding patterns and quality standards.
• Familiarity with knowledge representation approaches such as knowledge graphs or ontology-driven modeling, particularly where downstream consumers require traceability and consistent semantics.

We offer a competitive total rewards package including 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, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include 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. Additional details about total compensation and benefits will be provided during the hiring process.