We are seeking an experienced Data Scientist – Compliance (Banking) to develop, validate, and deploy advanced analytics and machine learning solutions that support regulatory compliance, financial crime prevention, and risk management initiatives within the banking environment. The ideal candidate will combine strong technical expertise in data science with deep knowledge of banking regulations, AML/KYC processes, sanctions screening, and model risk management frameworks. This role requires delivering robust, explainable, and audit-ready models that meet regulatory expectations and business objectives.
Requirements
Key Responsibilities
- Design, develop, validate, and monitor machine learning and statistical models for compliance, financial crime detection, and risk management use cases.
- Build predictive and anomaly detection models supporting AML, KYC, sanctions screening, transaction monitoring, customer risk scoring, and fraud detection programs.
- Ensure all models comply with internal governance standards, regulatory requirements, and model risk management frameworks.
- Perform model validation, performance testing, stability analysis, bias assessment, and explainability reviews.
- Develop and maintain comprehensive model documentation, validation reports, and audit-ready artifacts.
- Collaborate with Compliance, Risk, Financial Crime, Technology, and Data Engineering teams to translate business requirements into analytical solutions.
- Analyze large-scale structured and unstructured datasets to identify patterns, trends, and emerging compliance risks.
- Implement model monitoring frameworks, performance dashboards, and periodic model reviews.
- Support internal audits, regulatory examinations, and independent model validation exercises.
- Stay current with evolving regulatory requirements, industry best practices, and advancements in AI/ML technologies.
- Mentor junior data scientists and contribute to the establishment of data science best practices and governance standards.
Required Qualifications
- Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, Finance, or a related quantitative discipline.
- 6–8+ years of experience in Data Science, Machine Learning, or Advanced Analytics within Banking or Financial Services.
- Strong programming expertise in Python and SQL.
- Hands-on experience with machine learning libraries and frameworks including scikit-learn, XGBoost, PyTorch, and/or TensorFlow.
- Experience developing and validating classification, anomaly detection, forecasting, and risk-scoring models.
- Strong understanding of Model Risk Management (MRM) frameworks, model governance, and validation methodologies.
- Knowledge of banking regulatory requirements and compliance controls.
- Experience working with cloud and big-data ecosystems is an advantage.
Domain Expertise
- Anti-Money Laundering (AML)
- Know Your Customer (KYC)
- Customer Due Diligence (CDD) / Enhanced Due Diligence (EDD)
- Sanctions Screening
- Transaction Monitoring
- Financial Crime Risk Management
- Regulatory Compliance and Reporting
Preferred Skills
- Experience with explainable AI (XAI) techniques and model interpretability tools.
- Knowledge of regulatory expectations related to AI/ML model governance in financial institutions.
- Familiarity with MLOps, model deployment, monitoring, and lifecycle management.
- Strong analytical thinking, problem-solving, and stakeholder management skills.
- Excellent communication and documentation abilities with experience presenting to risk, compliance, audit, and senior leadership teams.
Success Metrics
- Delivery of compliant, accurate, and explainable machine learning models.
- Successful completion of model validation and regulatory reviews.
- Reduction in false positives and improved detection effectiveness in compliance programs.
- High-quality audit-ready documentation and governance adherence.
- Effective collaboration with Compliance, Risk, and Technology stakeholders.