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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