Associate Director, FCC Models & Product Risk

Airwallex•Amsterdam, Amsterdam

7d•Hybrid

About The Position

This role sits within the AML & Sanctions, Governance & Policy pillar of the FCC organization and serves as a critical bridge between regulatory intent and technical execution. In this role, you will serve as the global second-line authority and owner of financial crime detection models, risk analytics, and automated control frameworks. You will own the global methodology, thresholds, and data-scoring models used to identify and mitigate financial crime typologies, be accountable for model governance and liaison with Model Risk Management, and partner with Product Risk and CDO counterparts on technical control deployments as well as the FCC Investigations and FIU team for feedback loops on coverage and tuning optimization. This is a highly quantitative, technically fluent leadership position requiring deep comfort with production logic, automated systems, and data science architectures, balanced with a strong understanding of global AML/Sanctions compliance frameworks and data requirements. This role can be based in the US or EMEA.

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.

Nice To Haves

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.
Proven track record of moving advanced machine learning, neural networks, or behavioral anomaly detection models out of testing and into live, global compliance operations safely and transparently.
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.

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.