Associate Director, FCC Models & Product Risk

Responsibilities

• Lead the global design and implementation of risk assessment methodologies and quantitative detection models for all financial crime typologies, including money laundering, sanctions circumvention, and behavioral anomalies.
• Own the quantitative architecture of Airwallex's automated customer risk-rating engines and corporate screening matrices, ensuring behavioral data, risk vectors, and corporate structures are weighted dynamically in real time.
• Translate complex global compliance obligations into precise, logic-driven product requirements, algorithmic triggers, and risk engineering roadmaps that Product Risk and Engineering teams can build against.
• Establish and enforce the second-line risk governance framework for new product launches, market expansions, and system changes, ensuring automated compliance controls are fully tested and integrated prior to deployment.
• Direct the ongoing calibration and statistical tuning of transaction monitoring systems and screening thresholds, utilizing advanced data analytics to minimize false positives while maintaining high detection rates.
• Govern the deployment of advanced analytics within the compliance ecosystem, including machine learning models and graph databases, while establishing data governance standards to ensure input pipeline integrity.
• Establish a robust model validation framework to independently test and verify the conceptual soundness, mathematical logic, and regulatory compliance of all financial crime models.
• Represent Airwallex’s automated model infrastructure before global regulators and Tier-1 banking partners, defending threshold choices, machine learning methodologies, and tuning strategies with statistical evidence.

Requirements

• Bachelor’s degree in a highly quantitative or computational discipline such as Data Science, Computer Science, Statistics, Mathematics, Financial Engineering, or Econometrics.
• Practical fluency in big data environments, query languages (SQL), and algorithmic scripting languages (Python or R).
• 8 to 12+ years of total experience within financial institutions or technology platforms, with at least 4+ years specifically leading teams focused on FCC model governance, compliance analytics, or transaction monitoring optimization.
• Deep experience designing, configuring, or tuning enterprise-grade transaction monitoring and sanctions screening engines (proprietary or vendor-based).
• Proven track record of running data-driven threshold tuning, Above-The-Line/Below-The-Line (ATL/BTL) testing, and statistical segmentations to optimize detection logic.
• Demonstrated experience defending automated models, tuning choices, and risk-scoring methodologies to global regulators and institutional clearing bank partners.
• Experience establishing or scaling a model inventory and independent validation framework in line with international regulatory benchmarks.

Qualifications

• Bachelor’s degree in a highly quantitative or computational discipline such as Data Science, Computer Science, Statistics, Mathematics, Financial Engineering, or Econometrics.
• Master’s degree or Ph.D. in Data Science, Artificial Intelligence, Quantitative Finance, or a highly analytical MBA.
• CAMS-RM (Risk Management), ICA International Diploma in Financial Crime Risk, or technical certifications in Machine Learning or Big Data architectures.
• Direct experience managing financial crime models in a cloud-native, API-first environment where transaction decisions must be executed algorithmically in milliseconds.
• Experience utilizing graph databases and link analysis to identify complex, multi-layered financial networks and corporate structures.
• Experience leading cross-functional squads of data scientists, data engineers, and traditional compliance subject matter experts, acting as a translator between code, mathematics, and regulatory mandates.