Product Manager - AML
Product Manager who owns Oscilar's AML and financial-crime suite — transaction monitoring, KYC/KYB, sanctions screening, case management, and SAR filing — augmented by AI agents. An AI-native PM role: using AI tools for discovery, PRDs, UX exploration, and data analysis to build AI risk-decisioning products for banks and fintechs.
- Compensation
- $185K–$250K / yr
- Location
- United States
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Product
- Posted
Skills
Must have
- Full-stack AML product depth: shipped production product across at least two of transaction monitoring, KYC/KYB, screening, SAR filing, AML data strategy, under real regulatory scrutiny
- AML strategy knowledge: program design and calibration, scenario threshold drift, structuring vs layering typologies, model drift, backtesting methodology, model validation reports
- Technical and data fluency: read data schemas, write queries to validate hypotheses, spot misapplied ML models, and reason about explainability in AI-assisted compliance decisions
- Regulatory literacy: BSA, FinCEN, OFAC, FATF; examiner expectations, MRA triggers, defensible audit trails
- AI-native workflow: uses AI tools across discovery, PRD/spec writing, UX exploration, and data analysis
- Bias toward speed: uses AI tools to move faster, test sooner, and learn earlier
- Ability to engage compliance executives (BSA officers, heads of financial crimes, CCOs) on AML program design
Full description
Shape the future of trust in the age of AI
At Oscilar, we're building the most advanced AI Risk Decisioning™ Platform. Banks, fintechs, and digitally native organizations rely on us to manage their fraud, credit, and compliance risk with the power of AI. If you're passionate about solving complex problems and making the internet safer for everyone, this is your place.
Why join us:
- Mission-driven teams: Work alongside industry veterans from Meta, Uber, Citi, and Confluent, all united by a shared goal to make the digital world safer.
- Ownership and impact: We believe in extreme ownership. You'll be empowered to take responsibility, move fast, and make decisions that drive our mission forward.
- Innovate at the cutting edge: Your work will shape how modern finance detects fraud and manages risk.
The Role:
AML operations teams are making high-stakes decisions on bad tooling. False positive rates hitting 90–95%. SAR narratives written by hand. Alert queues backlogged for days. Investigation workflows duct-taped across a transaction monitoring system, a case management tool, and a spreadsheet no one owns.
AI has gotten good enough to fix most of this - but compliance teams don't trust it yet, and most AI vendors don't understand AML well enough to earn that trust. You will own the product that earns it.
At Oscilar, you'll have full product ownership of our AML and financial crimes suite: transaction monitoring, KYC/KYB, screening, case management, SAR filing, and the AI agents that are beginning to augment and automate the investigation workflow itself. This is not a handed-down playbook. You are writing it.
What You'll Own:
- Transaction Monitoring. The rules engine and ML model layer that generates alerts - scenario design, threshold calibration, typology coverage, backtesting against historical transaction data. You'll define what the system flags, why, and how those decisions evolve as customer behavior and fraud typologies shift. You understand the difference between a rules-based velocity check and a graph-based network detection model, and you have opinions on when each is right.
- KYC / KYB. Onboarding risk workflows: identity verification, document checks, beneficial ownership, ongoing monitoring triggers, and risk re-scoring. You know the data sources that power these decisions - bureau data, device signals, open banking, registry data - and how to orchestrate them dynamically based on risk tier rather than applying a one-size waterfall.
- Screening. Sanctions (OFAC, UN, EU), PEPs, adverse media, and negative news. You understand the matching problem - why fuzzy matching on names produces the false positive rates it does, and what it takes to make screening defensible to an examiner without generating alert volumes that bury the ops team.