Product Manager - AML

From the listing

About the role

What you'll do

• Own Transaction Monitoring: the rules engine and ML model layer that generates alerts, including scenario design, threshold calibration, typology coverage, and backtesting against historical transaction data. Define what the system flags, why, and how those decisions evolve.
• Own KYC/KYB onboarding risk workflows: identity verification, document checks, beneficial ownership, ongoing monitoring triggers, and risk re-scoring. Orchestrate data sources dynamically based on risk tier.
• Own screening for sanctions (OFAC, UN, EU), PEPs, adverse media, and negative news. Make screening defensible to an examiner without generating alert volumes that bury the ops team.
• Own SAR Filing & Regulatory Reporting: the FinCEN SAR workflow end-to-end, including narrative generation, supporting documentation, filing status, and audit trail.
• Own AML Data Strategy: transaction graphs, entity resolution, behavioral baselines, consortium signals, and model feature pipelines. Speak to data engineers about what data the models need and to compliance officers about what model outputs mean and how they were derived.
• Use AI for discovery to synthesize call transcripts, cluster themes across interviews, scan regulatory guidance and examiner findings, and surface competitive product gaps.
• Use AI to generate first-draft PRDs, stress-test specs, write and iterate on user stories, prototype UI flows, and pressure-test edge cases before engineering touches them.
• Use AI to explore multiple UX directions quickly and give the design team a sharper brief.
• Use AI to query product and operational data, interpret model outputs, and build the evidence base for roadmap decisions.
• Within 30 days, run discovery with at least 5 AML/compliance buyers, produce a written synthesis surfacing at least one new insight, and identify the two biggest gaps in the current AML product surface relative to buyer needs.
• Within 60 days, deliver one complete PRD reviewed by engineering, AML SMEs, and product and ready to build. Develop a clear point of view on SAR narrative automation and pressure-test it with at least two practitioners.
• Within 90 days, be the PM on calls with CCO prospects, have a 12-month AML roadmap with clear prioritization rationale, and ship at least one thing to production.

What they're looking for

• Full-stack AML product depth: shipped product across at least two of the five areas above, in production, with real users, under real regulatory scrutiny. Understand what is at stake when an examiner asks about your system and what a BSA officer worries about before an exam.
• AML strategy knowledge: understand how AML programs are designed and calibrated, why scenario thresholds drift and how to fix them, structuring and layering typologies, model drift, backtesting methodology, and model validation reports. Engage a Chief Compliance Officer on AML program design.
• Technical and data fluency: write specs engineers trust, read a data schema, write a query to validate a hypothesis, and spot when an ML model is being misapplied. Understand explainability in AI-assisted compliance decisions as a concrete product constraint.
• Regulatory literacy: BSA, FinCEN, OFAC, and FATF. Understand what examiners look for, what triggers an MRA, and how to build a defensible audit trail. Have worked where regulatory guidance was unclear, evolving, or in conflict with shipping speed.
• AI-native PM: use AI tools throughout your entire workflow as a force multiplier.
• Bias toward speed: use AI tools to move faster, test sooner, and learn earlier.

Benefits, as listed

1 of these also appear in the perks Jeff verified on the company page.