Principal Product Manager – Money Laundering Detection

Overview

As Principal Product Manager in Wise’s AML Squad, you will shape the next generation of real-time crime detection in a global payments environment. You collaborate with cross-functional teams to build high-precision, scalable systems that meet regulatory obligations while driving business value. You’ll dive into criminal typologies and regional patterns to improve recall, reduce SAR costs, and partner with regulators and compliance teams. This role offers wide impact across borders and a chance to influence how money moves securely and efficiently.

Responsibilities

• Define and own the product vision and roadmap for live, hyper-specific crime detection at scale
• Collaborate with regional compliance, data scientists, engineers, designers, and analysts to deliver high-precision detection products
• Improve AML Recall and optimize Cost of SAR through automation and advanced technology
• Engage with regulators and law enforcement to ensure global controls and regulatory alignment
• Translate complex regulatory and business needs into tangible building blocks with a clear definition of done
• Move fast with autonomy while maintaining strong governance and cross-functional alignment

Key requirements

• 6+ years of direct product management experience for Principal level (or 4–8 years for Senior level)
• Customer-obsessed mindset with ability to empathize with regulators and law enforcement as customers
• Direct experience building machine learning, LLM, or data-heavy products
• Ability to use qualitative and quantitative data to diagnose problems and prioritize opportunities
• Experience turning a vision into actionable roadmaps and measurable outcomes
• Extensive experience working in cross-functional teams of engineers, designers, data scientists, and researchers
• Hands-on, autonomous, and comfortable operating in ambiguity
• Understanding of AML, fraud, or related domains is a plus

• customer-obsessed
• self-driven
• ambiguous environment navigation
• machine learning
• LLMs
• data-heavy product development

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