• Decompose high-level features / requirements into user stories, acceptance criteria, and clear backlog prioritization.
• Collaborate with engineering, data science, and solutions teams to deliver high-quality TM features (real-time and batch pipelines, detection analytics, alert scoring, alert/ case creation, feedback loops).
• Drive backlog grooming, sprint planning, and manage priorities / dependencies across multiple engineering pods.
• Participate in design reviews, technical estimation, and ensure non-functional requirements (performance, scalability, security, auditability) are included in scope.
• Support calibration, back-testing, champion / challenger model assessment, model/ rule threshold tuning, monitoring of false positives / recall and improving true positive detection.
• Ensure explainability, reason codes, lineage, and model transparency across TM solution, including detection analytics and alert / risk scoring modules.
• Work with engineering teams and customers to define test cases, regression test suites, and oversee UAT / release validation.
• Monitor metrics (alert volumes, false positives, investigator throughput, conversion rates) and iterate to optimize.
• Manage dependencies with external systems (watchlists, screening, sanctions feeds, data enrichment, client data ingestion).
• Create or update product documentation, user guides, runbooks, and support internal teams (sales, services, support).
• Stay current on AML regulations, typologies and trends across the financial crime sector.
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Requirements

Required Qualifications & Skills

• 6+ years of experience in product, business analysis, or domain roles, with at least 4 years working in AML / financial crime / risk / compliance products or operations.
• Deep understanding of AML transaction monitoring, financial crime typologies and risks. Understands case management flows and regulatory expectations.
• Experience working on systems that handle regulated, mission-critical workflows (auditability, traceability, security).
• Familiarity with data processing and analytics: pipelines, feature engineering, model scoring, streaming vs batch.
• Comfortable crafting user stories, acceptance criteria, and working in Agile / Scrum methodologies.
• Strong stakeholder management skills: able to mediate between compliance, operations, engineering, and clients.
• Excellent written and verbal communication; capable of explaining technical & domain complexity to business and technical audiences.
• Analytical mindset, with data-driven decision making and ability to measure outcomes.
• Degree in Computer Science, Engineering, Data Science, or equivalent; or extensive domain equivalent experience.
• CAMS or equivalent certification is preferred