Senior Lead Data Scientist – AML & Financial Crime Analytics

Description

Senior Lead Data Scientist – AML & Financial Crime Analytics

Position Overview

We are seeking a highly experienced Senior Lead Data Scientist to lead the development of advanced analytics and machine learning solutions supporting Anti-Money Laundering (AML), Financial Crime Compliance, Fraud Detection, and Enterprise Risk Management initiatives. This role will drive the design, deployment, and optimization of predictive models that identify suspicious activity, mitigate financial crime risk, and enhance regulatory compliance.

The ideal candidate combines deep expertise in data science, machine learning, statistical modeling, and financial crime domains, with the ability to translate complex business challenges into scalable analytical solutions.

Key Responsibilities

Machine Learning & Advanced Analytics

• Design, develop, validate, and deploy machine learning models for:
• Anti-Money Laundering (AML)
• Fraud Detection
• Transaction Monitoring
• Risk Scoring and Customer Risk Rating
• Customer Segmentation
• Anomaly Detection
• Financial Crime Risk Assessment
• Apply advanced statistical techniques, predictive analytics, Bayesian modeling, and quantitative methodologies to identify suspicious patterns, emerging threats, and operational risks.
• Build and maintain end-to-end model development frameworks, including:
• Data preparation and feature engineering
• Model training and validation
• Hyperparameter optimization
• Model deployment and monitoring
• Performance assessment and recalibration
• Conduct large-scale data mining, exploratory data analysis, model benchmarking, and sensitivity testing to improve model effectiveness and regulatory defensibility.

Financial Crime & Risk Analytics

• Develop and enhance analytical models supporting AML investigations, sanctions screening, customer due diligence (CDD), enhanced due diligence (EDD), and fraud prevention programs.
• Design simulation and risk assessment models to evaluate financial crime exposure and support compliance decision-making.
• Analyze transactional, customer, behavioral, and network data to identify hidden relationships, suspicious activities, and emerging financial crime patterns.
• Support model governance, validation, documentation, and regulatory examination requirements.

Business Partnership & Leadership

• Partner closely with Compliance, AML Operations, Financial Crime, Fraud, Risk, Audit, and Technology teams to define analytical requirements and deliver actionable solutions.
• Translate complex business and regulatory requirements into scalable data science and machine learning solutions.
• Present analytical findings, model performance, and strategic recommendations to senior leadership, compliance executives, and regulators when required.
• Mentor junior data scientists and analytics professionals while promoting best practices in machine learning, statistical analysis, and model governance.
• Drive innovation through the adoption of emerging AI, machine learning, and advanced analytics technologies.

Required Qualifications

• Master's degree or Ph.D. in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, Physics, or another quantitative discipline.
• 8+ years of experience in Data Science, Machine Learning, Predictive Analytics, or Quantitative Modeling.
• Strong experience developing machine learning models using Python, R, SQL, Spark, or similar technologies.
• Expertise in classification, clustering, anomaly detection, network analytics, predictive modeling, and statistical inference.
• Proven experience supporting one or more of the following areas:
• Anti-Money Laundering (AML)
• Know Your Customer (KYC)
• Customer Due Diligence (CDD/EDD)
• Sanctions Screening
• Transaction Monitoring
• Fraud Detection and Prevention
• Financial Crime Compliance
• Enterprise Risk Management
• Strong knowledge of AML regulatory requirements, financial crime typologies, and compliance frameworks.
• Experience with model risk management, model validation, governance, documentation, and regulatory audit requirements.
• Excellent communication and stakeholder management skills, with the ability to present complex analytical concepts to technical and non-technical audiences.

Preferred Qualifications

• Experience with graph analytics, network analysis, and entity resolution techniques.
• Hands-on experience with cloud analytics platforms such as Azure, AWS, GCP, Databricks, or Snowflake.
• Knowledge of Generative AI and AI-driven financial crime detection solutions.
• Experience working within global