Vice President - Data Science/Applied AI ML

Lead the CCOR Financial Crime Data Science team to design, deploy, and operate production-grade ML solutions across AML transaction monitoring 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, 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, and governance artifacts to ensure reliable, scalable production operations. Partner with Financial Crime Compliance (FCC), Investigations, Operations, and Technology to translate typologies, red flags, and regulatory expectations into defensible ML controls and measurable control effectiveness. Enhance investigator decisioning through interpretable ML: deploy explainability techniques, stable reason codes, and human-in-the-loop feedback loops to continuously improve model precision and usability. Mentor, hire, and develop a high-performing team of data scientists/ML engineers/analysts; promote a culture of scientific rigor, ethical AI, and continuous learning. Maintain a pragmatic view of GenAI/LLMs as complementary tools while prioritizing classical/statistical/graph ML methods for core detection efficacy.