Senior Data Scientist - Financial Crime and AML

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/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 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:

• Master’s 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, 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, 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.

 

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Experience Level

Mid Level