Senior Data Scientist II – Fraud Prevention

About the role

LexisNexis Risk Solutions is looking for a strategic Senior Data Scientist to lead fraud‑prevention initiatives across multiple regions. You will shape analytical capabilities, drive innovation, and deliver scalable solutions that protect digital identities and support next‑generation fraud strategies.

Key responsibilities

• Design, develop, validate, and monitor fraud‑prevention models, machine‑learning solutions, and analytical frameworks.
• Drive analytical initiatives aligned with product, technology, and commercial goals for next‑generation fraud strategies.
• Partner with Product, Technology, Sales, Pre‑Sales, Content Acquisition, and Global Analytics teams to deliver scalable business solutions.
• Identify and implement enhancements for fraud detection, entity resolution, identity verification, and data intelligence.
• Support Decision Trust solutions through customized analytics, proof‑of‑concepts, customer engagements, and production implementations.
• Provide technical leadership and mentorship to data‑science peers, promoting best practices.
• Ensure compliance with enterprise governance, Responsible AI principles, and regulatory requirements.
• Communicate findings and recommendations to both technical and non‑technical stakeholders, including senior leadership.
• Contribute to the long‑term analytical roadmap and evaluate emerging data assets and methodologies.

Required profile

• Advanced experience in Data Science, Advanced Analytics, or Machine Learning.
• Strong background in fraud analytics, digital identity, risk modeling, or financial‑crime prevention.
• Proven ability to develop and deploy analytical solutions in production environments.
• Excellent stakeholder‑management and communication skills, with influence across technical and business teams.
• Experience in global, matrixed organizations and ability to balance strategic thinking with hands‑on execution.
• Advanced English communication skills; experience with identity intelligence or fraud‑prevention platforms is a plus.

Required skills

• Predictive modeling
• Machine learning
• Statistical analysis
• Large‑scale data environments