Senior Associate Applied AI ML Data Science

Job Responsibilities:

• 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/semisupervised 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 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 (e.g. SHAP LIME counterfactuals) 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 (e.g. narrative generation for cases unstructured doc parsing) while prioritizing classical/statistical/graph ML methods for core detection efficacy.

  Required Qualifications and Skills:

• Masters or PhD in a quantitative discipline (Computer Science Statistics Mathematics Economics Operations Research or related).
• Minimum of 6 years of hands-on ML experience with at least 3 years in Financial Crime Compliance AML sanctions fraud or related risk domains; deep knowledge of regulatory expectations (e.g. AML program requirements sanctions controls model governance).
• Proven leadership delivering production ML for financial crime 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 ML frameworks.
• Expertise in supervised learning anomaly detection semisupervised 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 backtesting 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.
• People leadership: recruiting coaching performance management and fostering an inclusive high-accountability culture.