Vice President - Data Science / Applied AI ML

Lead the CCOR Conduct Data Science initiatives to design, deploy, and operate production-grade GenAI/AI/ML solutions across risk and compliance use cases, with a strong focus on measurable risk mitigation and regulatory alignment. Drive research and applied innovation in supervised/unsupervised/semi‑supervised learning, graph/network analytics, anomaly detection, and weak supervision to improve true-positive rates, reduce false positives, and enhance investigator productivity. Own end-to-end model lifecycle: problem framing, data sourcing/controls, feature engineering (customer/behavioral/temporal/graph features), model development, validation, calibration/thresholding, bias/fairness checks, monitoring, and retraining. Maintain rigorous model risk management practices across Model lifecycle, partnering with Model Risk and Internal Audit. Build and maintain robust MLOps pipelines (CI/CD for ML), model registries, automated monitoring (data drift, concept drift, performance), and governance artifacts to ensure reliable, scalable production operations. Partner with Risk and Compliance (RCC), Investigations, Operations, and Technology to translate typologies, red flags, and regulatory expectations into defensible ML controls and measurable control effectiveness. Enhance decisioning through interpretable ML: deploy explainability techniques (e.g., SHAP, LIME, counterfactuals), stable reason codes, and human-in-the-loop feedback loops to continuously improve model precision and usability. Maintain a pragmatic view of GenAI/LLMs as complementary tools (e.g., narrative generation for cases, unstructured doc parsing) while prioritizing classical/statistical/graph ML methods for core detection efficacy. Required Qualifications and Skills: Master’s or PhD in a quantitative discipline (Computer Science, Statistics, Mathematics, Economics, Operations Research, or related). Minimum of 7 years of hands-on Gen AI/ AI/ ML experience within Financial Crime Compliance, AML, sanctions, fraud, or related risk & compliance domains; deep knowledge of regulatory & control expectations. Proven leadership delivering production AI/ML for compliance & risk, including transaction monitoring models, risk scoring, anomaly detection, network/graph analytics, and/or investigator triage/prioritization at enterprise scale. Advanced Python skills; strong experience with AI/ML frameworks. Expertise in supervised learning, anomaly detection, semi‑supervised learning, clustering, feature stores, and calibration/threshold optimization; familiarity with imbalanced learning and cost-sensitive evaluation. Demonstrated experience in model risk management: documentation, validation, benchmarking/challenger models, back testing, stability and drift analysis, champion/challenger governance, and explainability suitable for regulatory review. Excellent communication skills to translate and explain complex models with clear reason codes, and influence cross-functional stakeholders and senior leadership. Ability to mentor junior team members through code reviews, pairing, and technical guidance