Associate Director-Product Manager (Financial Crimes)

Overview

In this role, you lead the Knowledge Graph platform to unify data and empower GenAI-enabled analytics across Moody’s. You will partner with data, platform, and business teams to translate problems into platform capabilities and a clear backlog. You drive adoption, define criteria for evaluating graph technologies, and steer build-vs-buy decisions while shaping a responsible AI program. This is a chance to scale data infrastructure that underpins risk insights and smarter decision-making.

Responsibilities

• Lead product management for the enterprise Knowledge Graph platform, coordinating with data and engineering teams
• Advance Risk Insights solutions through platform-scale initiatives and GenAI applications
• Evaluate Knowledge Graph technologies via proofs of concept and trade-off documentation; guide build-vs-buy decisions
• Source and prioritize Knowledge Graph use cases with transparent value hypotheses and metrics
• Translate business problems into precise functional and non-functional requirements (ontology scope, data sources, latency, security)
• Own the product roadmap; run agile ceremonies and manage cross-team dependencies
• Serve as primary liaison for sponsors, domain experts, and product teams; communicate risks and trade-offs
• Define onboarding paths for new use cases and drive platform adoption to support continuous improvement

Key requirements

• 5+ years in sanctions, AML, fraud investigations, or related regulatory spaces with deep domain knowledge
• Strong ability to read schemas, review queries, and discuss data modeling trade-offs with engineers
• Understanding of graph data modeling (nodes, edges, ontology, RDF vs property graphs) and entity resolution
• Proven track record translating ambiguous problems into engineering requirements (functional/non-functional)
• Excellent stakeholder management to align sponsors, SMEs, and engineering leads around a plan
• Strong project management skills with agile/scrum, backlog hygiene, dependencies, and risk management

• stakeholder management
• communication with both technical and non-technical audiences
• ability to translate business problems into technical requirements
• Graph data modeling concepts (nodes, edges, properties, ontologies, taxonomies)
• RDF vs property graphs; entity resolution
• Knowledge Graph technologies evaluation; proofs of concept

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