Product Manager

Responsibilities

• Define and drive the AML Monitoring Platform product vision, strategy, and multi-year roadmap.
• Establish a scalable, enterprise-grade data foundation for transaction monitoring and financial-crime detection.
• Identify and prioritize high-value use cases (rule-based detection, anomaly detection, alert prioritization, regulatory reporting, AI/ML).
• Evangelize the AML platform vision across compliance, business, and leadership stakeholders.
• Own end-to-end delivery of the AML data product lifecycle:
  • Canonical, normalized transaction and party data models
  • Detection rule engine and AML typology coverage
  • Alert and case data feeding investigator workflows
  • Risk scoring, segmentation, and entity relationship resolution
• Translate complex compliance and business needs into clear, actionable product and data requirements.
• Define KPIs, success metrics, and product SLAs (e.g., detection coverage, false-positive rate, alert-to-case conversion, time-to-disposition).
• Partner with senior stakeholders across:
  • Compliance, Financial Crime, Investigations, Risk, and Business SMEs
  • Data Engineering, Architecture, Governance, and Analytics/ML teams
• Act as a strategic bridge between business/compliance and technical organizations.
• Influence decision-making at the leadership level.
• Collaborate with engineering teams to design:
  • Scalable data pipelines (ETL/ELT) across ingestion, curation, and detection layers
  • Modern data architectures (medallion, lakehouse, warehouse)
  • Batch, scheduled, and near-real-time processing capabilities
• Ensure alignment with enterprise data platforms (e.g., Snowflake, Azure, Databricks) and integration with downstream case-management systems.
• Drive data governance frameworks for AML data:
  • Standard definitions, taxonomies, and metadata
  • Data lineage, stewardship, and ownership
• Ensure data quality, consistency, auditability, and compliance with AML and privacy regulations.
• Manage master data, entity resolution, and identity resolution strategies critical to accurate detection.
• Enable advanced capabilities such as:
  • Alert dashboards, investigator triage queues, and executive/regulatory reporting
  • Detection-performance, typology-trend, and false-positive analysis
  • Threshold calibration, peer-group benchmarking, and segmentation
• Partner with teams on AI/ML-driven use cases (e.g., anomaly detection, risk scoring, alert prioritization, model explainability).
• Lead Agile delivery (backlog prioritization, sprint planning, releases).
• Manage trade-offs across scope, timeline, and quality.
• Track adoption, usage, and business impact; iterate continuously.

Requirements

• Bachelor's or Master's degree in Computer Science, Data, Engineering, Business, or related field.
• 8+ years of experience in:
  • Data Product Management / Product Management / Data & Analytics
• Proven experience delivering enterprise-scale data platforms or financial-crime / AML / transaction-monitoring solutions.
• Strong understanding of:
  • Data modeling, data warehousing, and distributed data systems
  • ETL/ELT pipelines and integration patterns
• Hands-on experience with:
  • SQL and BI tools (Power BI, Tableau, Looker, etc.)

Preferred Qualifications

• Experience with AML, transaction monitoring, fraud detection, KYC, or financial-crime compliance initiatives.
• Familiarity with case-management and investigation systems.
• Experience with modern data platforms:
  • Snowflake, Azure Data Platform
• Knowledge of AML regulatory, data governance, privacy, and compliance frameworks (e.g., BSA, SAR/STR reporting, sanctions screening).
• Agile/Scrum certification or strong Agile delivery experience.

Skills

• Strategic thinking with strong execution focus
• Deep data and analytics expertise
• Exceptional stakeholder management and executive communication
• Ability to influence without authority
• Strong problem-solving and decision-making skills
• Risk-aware, compliance-centric mindset