Senior Data Scientist

Overview

Data Scientist - Compliance (Banking)

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.

• 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