Lead complex data science projects, ensuring they meet business objectives and deliver actionable insights. Develop advanced data models and algorithms to analyze large datasets and solve complex business problems. Collaborate with senior leadership to identify data-driven opportunities for business growth and efficiency. Implement best practices for data management, analysis, and visualization. Ensure data governance and compliance with relevant regulations and standards. Provide mentorship and technical guidance to the data science team. Minimum of 12 years of relevant work experience and a Bachelor's degree or equivalent experience. Previous management experience The GFC Analytics organization is looking for an incredibly talented, self-motivated and analytical individual to join the Detections & Oversight Team. This person will be responsible for development of AML and Brand Risk Management (BRM) transaction monitoring rules to provide risk coverage, as well as tuning and optimization of transaction monitoring rules to improve efficiency and effectiveness of the rules, thereby supporting the Global Anti-Money Laundering/Counter Terrorist Financing (AML /CTF) policy, BRM policy to detect Acceptable Use Policy (AUP) violations, and other GFC strategic initiatives. The successful candidate will possess deep expertise in data and statistical analysis techniques, as well as GFC policies and understanding of global AML regulations. 10+ years of experience in data science, machine learning, or quantitative analytics, with at least 4 years in AML, fraud detection, or financial crime compliance. Proven ability to design, validate, and deploy predictive or anomaly detection models in production. Strong knowledge of transaction monitoring systems, AML regulations (e.g., BSA/AML, FATF, FinCEN), and compliance best practices. Hands-on experience with Python, SQL, and modern ML frameworks (e.g., scikit-learn, XGBoost, TensorFlow, or PyTorch). Experience managing data scientists and cross-functional projects in highly regulated financial environments. Excellent communication skills and a demonstrated ability to bridge technical solutions and regulatory requirements. Advanced degree (MS or PhD) in Computer Science, Statistics, Mathematics, or a related quantitative field strongly preferred.
