About the job

As a part of the Google Payments Compliance AML team, you will work to safeguard Google's users and protect our products from fraud and financial crimes abuse by ensuring strict adherence to global Anti-Money Laundering (AML)/Counter-Terrorist Financing (CTF) regulations. You will partner closely with Regional Compliance, Payments Platform, and Engineering teams to manage financial crime risk across Google's payments ecosystem. You will be responsible for designing, implementing, and optimizing the AML program, processes, and operations that ensure Google maintains regulatory compliance.In this role, you will be responsible for the global design, development, and management of Google Payments' systemic AML/CTF transaction monitoring program. You will lead the end-to-end life-cycle of our monitoring "triggers" (rules and models), from initial risk assessment and data analysis to development, tuning, and governance.

You will be responsible for driving the continuous improvement of our detection capabilities. You will blend deep AML subject matter expertise with strong quantitative and coding skills. You will be responsible for ensuring the monitoring program is effective, efficient, and demonstrably compliant with regulatory expectations and internal model risk management standards.
The US base salary range for this full-time position is $147,000-$216,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Develop, test, and deploy new rule-based and statistical AML detection mechanisms to address financial crime risks in existing and new products.

• Drive the end-to-end ongoing tuning and optimization life-cycle for existing triggers to improve detection accuracy and reduce false positives, balancing risk effectiveness with operational efficiency.

• Own and enhance the governance framework, including comprehensive documentation of trigger logic, data lineage/validation, and tuning outcomes to support internal audits and regulatory exams.

• Research, pilot, and productionalize innovative monitoring techniques, including the application of machine learning (ML) and AI, to enhance existing rule-based systems.

• Lead collaboration with cross-functional partners, including Engineering, Regional Compliance Officers (RCOs), and the Financial Crimes Intelligence teams, to design, propose, and operationalize new monitoring strategies while building and maintaining data pipelines, dashboards, or data solutions for the AML Investigations team.

Minimum qualifications

• Bachelor’s degree in a quantitative field (e.g., Statistics, Computer Science, Economics, Mathematics) or equivalent practical experience.

• 5 years of experience in an AML/Financial Crime compliance, data science, or quantitative risk management role.

• 5 years of experience developing, tuning, and validating AML transaction monitoring systems or abuse detection models.

• Experience in data analysis and modeling using SQL (for data extraction) and Python or R (for statistical analysis).

• Experience leading cross-functional quantitative projects.

Preferred qualifications

• Master’s degree or PhD in a quantitative discipline.

• 8 years of experience in an AML/BSA investigative environment, preferably within a global payments, financial technology, or technology company.

• Experience with SQL, Python, and applications to machine learning (e.g., clustering, anomaly detection, supervised models) for AML or fraud detection.

• Experience in payments data, payment processing, and financial crime typologies with an understanding of model risk management (MRM) principles.

• Experience in payments data, payment processing, and financial crime typologies.

• Ability to explain quantitative concepts to technical and non-technical audiences, including executive leaders with excellent communication and stakeholder management skills.