Senior Data Scientist

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.

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

• Bachelors or Masters degree in Data Science Statistics Computer Science Mathematics Engineering Finance or a related quantitative discipline.
• 68 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.

Required Experience:

Senior IC