Senior Associate - Data Science / Applied AI ML

Deliver production AI/ML solutions for CCOR Conduct risk & compliance use cases by translating typologies, red flags, and control objectives into measurable model outcomes (e.g., precision/recall improvements, false-positive reduction, investigator efficiency). Drive & Execute 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. Develop and enhance detection models using supervised/unsupervised/semi-supervised approaches (e.g., anomaly detection, clustering, weak supervision) and, where applicable, graph/network analytics to identify complex patterns and relationships. Execute key parts of the model lifecycle: data sourcing (with appropriate controls), feature engineering (behavioral/temporal/entity/link features), model training, evaluation, calibration/thresholding, and performance monitoring. Implement interpretable ML and human-in-the-loop workflows by supporting explainability (e.g., SHAP/LIME), stable reason codes, and feedback loops with investigators to improve usability and model precision over time. Contribute to MLOps and scalable deployment by partnering with technology teams on CI/CD for ML, model registry usage, automated monitoring (data drift/concept drift), and repeatable, well-governed release processes. Support model risk management (MRM) deliverables by producing documentation and analysis needed for validation (assumptions, limitations, benchmarking/challengers, back-testing, stability/drift analysis) and addressing review feedback. Collaborate across stakeholders (RCC, Investigations, Operations, Technology) to align on requirements, data readiness, controls, and target operating model for sustained production support. Apply GenAI/LLMs pragmatically (e.g., case narrative generation, unstructured text extraction/summarization) while prioritizing classical/statistical/graph ML methods where they deliver stronger, defensible detection efficacy. Required qualifications, capabilities, and skills: Master’s degree (or PhD preferred) in a quantitative discipline (Computer Science, Statistics, Mathematics, Economics, Operations Research, or related). Minimum 4 years of hands-on AI/ML experience, preferably with exposure to financial crime compliance / conduct risk / AML / fraud / sanctions or similar control environments. Demonstrated experience building and/or deploying ML solutions (risk scoring, anomaly detection, triage/prioritization, NLP/LLM-enablement) with a focus on measurable outcomes. Strong Python skills and experience with modern ML frameworks (e.g., PyTorch/TensorFlow) and common data/ML tooling. Practical knowledge of: imbalanced learning, cost-sensitive evaluation, feature engineering, model calibration/threshold optimization, and performance measurement in detection settings. Working knowledge of MRM expectations (documentation, validation support, explainability, monitoring) in regulated financial services environments. Clear communication skills—able to explain model behavior, tradeoffs, and outputs (including reason codes) to technical and non-technical stakeholders. Ability to mentor junior team members through code reviews, pairing, and technical guidance.