About the role:

We are seeking a Senior Data Scientist with deep expertise in financial fraud detection and claim abuse. This role will lead the design and implementation of advanced analytics and machine learning solutions to combat evolving fraud and dispute schemes. The ideal candidate will have a proven track record of delivering high-impact models, mentoring junior team members, and influencing strategic decisions across the organization.

Responsibilities include:

Strategic Leadership

• Drive the roadmap for internal AI & ML initiatives, aligning with business objectives and regulatory requirements.

• Serve as a subject matter expert on fraud analytics, advising leadership on emerging threats and mitigation strategies.

Advanced Modeling

• Architect and deploy cutting-edge machine learning and AI models to maximize fraud recovery, behavioral analysis, and predictive fraud prevention.

• Optimize models for scalability, real-time performance, and minimal false positives.

Data Innovation

• Identify new data sources and develop advanced feature engineering techniques to enhance fraud detection capabilities.

• Lead research into novel algorithms, including graph analytics and deep learning approaches.

Collaboration & Influence

• Partner with Legal, Leadership, and Engineering teams to integrate solutions into enterprise systems.

• Communicate complex technical concepts to non-technical stakeholders and influence decision-making.

Mentorship

• Guide and mentor junior data scientists, fostering a culture of innovation and continuous learning.

Governance & Compliance

• Ensure models adhere to regulatory standards (OCC, PCI DSS) and ethical AI practices.

Required Qualifications:

• 7+ years of experience in data science, with at least 3 years focused on fraud detection or financial risk analytics.
• Expertise in machine learning frameworks (scikit-learn, TensorFlow, PyTorch) and advanced statistical modeling.
• Strong proficiency in Python, SQL, and Snowflake
• Deep understanding of financial systems, payment networks, and fraud typologies.
• Experience deploying models in production environments and working with real-time streaming platforms.

Preferred Qualifications:

• Familiarity with graph databases and network analysis for fraud rings.
• Knowledge of cloud platforms (AWS) and MLOps best practices.
• Exceptional communication and leadership skills.