Lead Data Scientist (Financial Services)

Role Overview

We are seeking a Lead Data Scientist to support financial crime and AML (Anti-Money Laundering) initiatives in a client-facing consulting environment.

This role combines advanced analytics, stakeholder engagement, and delivery leadership. The ideal candidate will work closely with financial services clients to design and implement data-driven solutions that detect, prevent, and investigate financial crime.

The position emphasizes problem structuring, model oversight, and business impact, rather than purely hands-on model development.

Key ResponsibilitiesClient Engagement & Problem Structuring

• Act as a primary analytics lead for client engagements in financial crime (AML, fraud, sanctions).
• Work with business stakeholders to understand:
• Regulatory requirements
• Risk frameworks
• Current detection and monitoring processes
• Translate business problems into analytical approaches and solution frameworks.
• Present findings, recommendations, and model outputs to both technical and non-technical audiences.
  

Financial Crime Analytics & Model Development

• Design and guide development of models for:
• Transaction monitoring (AML detection)
• Fraud detection
• Customer risk scoring
• Network/graph-based anomaly detection
• Apply techniques such as:
• Supervised and unsupervised learning
• Anomaly detection
• Clustering and segmentation
• Graph analytics
• Ensure models are explainable and aligned with regulatory expectations.
  

Delivery Leadership & Team Coordination

• Lead delivery of analytics workstreams across:
• Data scientists
• Data engineers
• Business analysts
• Break down projects into clear deliverables and timelines.
• Review model performance, validation outputs, and documentation.
• Ensure high-quality, production-ready deliverables.
  

Data Strategy & Engineering Collaboration

• Work with data engineering teams to define:
• Data pipelines and ingestion strategies
• Feature engineering approaches
• Data quality and governance standards
• Ensure availability of high-quality datasets for model development.
  

Model Governance & Regulatory Alignment

• Ensure compliance with regulatory expectations (e.g., SR 11-7, model risk management).
• Support model validation, documentation, and audit readiness.
• Design models with interpretability and transparency in mind.
  
  

Continuous Improvement & Innovation

• Identify opportunities to improve:
• Detection accuracy
• False positive reduction
• Operational efficiency
• Introduce advanced techniques such as:
• Graph-based detection
• Behavioral analytics
• AI/ML enhancements to rule-based systems
  

Required Qualifications

• 7–10 years of experience in data science, analytics, or machine learning.
• Experience working in financial services or consulting environments.
• Strong background in financial crime, AML, fraud, or risk analytics.
• Proven experience in client-facing roles.
• Strong understanding of:
• Machine learning techniques
• Statistical modeling
• Data manipulation (Python, SQL)
• Experience leading or coordinating cross-functional teams.
  

Preferred Qualifications

• Experience with:
• Transaction monitoring systems
• Case management tools
• Graph databases (e.g., Neo4j)
• Familiarity with:
• Regulatory frameworks (AML, KYC, sanctions)
• Model risk management and validation processes
• Experience with cloud platforms (AWS, Azure, or GCP).
• Exposure to GenAI use cases in financial crime (e.g., case summarization, investigator copilots).
  

What Success Looks Like

• Clients view you as a trusted analytics advisor.
• Analytical solutions drive measurable improvements in:
• Detection rates
• False positive reduction
• Investigation efficiency
• Teams are aligned and delivering high-quality outputs.
• Models are robust, explainable, and compliant with regulatory standards.
  

Why This Role Matters

Financial institutions face increasing pressure to detect and prevent financial crime while maintaining operational efficiency and regulatory compliance. This role plays a critical part in designing and delivering data-driven solutions that enhance risk detection capabilities and enable smarter decision-making.

You will help bridge the gap between advanced analytics and real-world financial crime operations, ensuring solutions are both technically sound and practically impactful.

Skills: advanced,financial services,aml,risk