Senior Associate - Data Science / Applied AI ML

Job Responsibilities

• 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.