Associate Data Scientist - Fraud Analytics

We are seeking a highly analytical and creative Associate Data Scientist to join our Advanced Analytics and AI team focused on fraud detection and risk mitigation within our long term care insurance business. This role offers the opportunity to develop cutting-edge models and innovative solutions that directly protect our organization and policyholders from fraudulent activities.

Position Responsibilities: 

Model Development & Analytics

• Design and build sophisticated fraud detection models with emphasis on time series analysis to identify temporal patterns and trends in fraudulent behavior
• Develop anomaly detection systems to flag unusual claims patterns, provider behaviors, and policyholder activities
• Create graph-based models to uncover fraud rings, provider networks, and suspicious relationship patterns
• Build ensemble models that combine temporal, network, and statistical approaches for comprehensive fraud detection
• Perform advanced statistical analysis on large, complex datasets to uncover fraud indicators

Digital Controls & Innovation

• Design and implement digital controls and automated workflows to mitigate fraud impact
• Develop innovative analytical solutions to address emerging fraud schemes and attack vectors
• Create data-driven business rules and decision frameworks for fraud prevention
• Build monitoring systems and dashboards to track model performance and fraud trends

Research & Continuous Improvement

• Conduct time series analysis to identify seasonal fraud patterns, emerging trends, and change points in fraudulent activities
• Apply graph mining techniques to discover new fraud networks and relationship patterns
• Research and implement state-of-the-art anomaly detection methods for evolving fraud schemes
• Experiment with novel approaches including graph neural networks, temporal anomaly detection, and multivariate time series analysis
• Collaborate with business stakeholders to understand evolving fraud challenges

Collaboration & Communication

• Partner with claims, underwriting, and compliance teams to implement analytical solutions
• Present findings and recommendations to senior leadership and cross-functional teams
• Document methodologies, model logic, and analytical processes for regulatory compliance
• Contribute to team knowledge sharing and collaborative problem-solving

Required Qualifications:

• 2-4 years of experience in data science, analytics, or machine learning roles
• Experience with fraud detection, risk analytics, or financial crime prevention preferred
• Master's degree in Statistics, Mathematics, Physics, Engineering, Computer Science, or other quantitative science discipline

Technical Skills

• Advanced proficiency in Python or R for statistical analysis and machine learning
• Expert-level SQL skills and experience with database management systems
• Hands-on experience with machine learning frameworks
• Proficiency with graph analytical methods and libraries
• Experience with time series methods and libraries

Analytical Capabilities - Core Requirements

• Time Series Analysis: Demonstrated expertise in time series forecasting, trend analysis, seasonality detection, and change point detection. Experience with ARIMA, state space models, and modern deep learning approaches for temporal data
• Anomaly Detection: Strong background in outlier detection methodologies including statistical approaches, machine learning methods and deep learning techniques
• Graph Methods: Proven experience with network analysis, community detection, centrality measures, and graph-based fraud detection. Knowledge of graph neural networks and link prediction algorithms
• Advanced understanding of unsupervised learning, clustering, and dimensionality reduction techniques
• Strong foundation in statistical modeling, hypothesis testing, and experimental design
• Understanding of model validation, performance metrics, and bias detection

Preferred Qualifications

• Advanced Graph Analytics: Experience implementing graph-based fraud rings detection, money laundering networks, and provider