Senior Data Scientist

Job Description

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.

Responsibilities

• 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.

Skills Required