Associate Data Scientist - Fraud Analytics

Overview

We are seeking a highly analytical and creative Associate Data Scientist to join our advanced analytics team focused on fraud detection and digital 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 while ensuring legitimate claims are processed efficiently.

• 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
• Leverage large language models (LLMs) for analyzing unstructured claims data, policy documents, and investigator notes to identify fraud indicators

• 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

• Deploy and monitor machine learning models in production environments using MLOps best practices
• Implement model versioning, A/B testing, and continuous integration/deployment pipelines for fraud detection systems
• Design real-time model serving infrastructure for low-latency fraud scoring
• Establish model performance monitoring, drift detection, and automated retraining workflows
• Collaborate with engineering teams on scalable AI system architecture and deployment strategies

• Master’s degree or PhD degree in quantitative fields such as Statistics, Applied Mathematics, Data Science, Engineering, or Computer Science or Physics.
• Proficient in programming using Python and SQL.
• At least 2-year of industry experience in developing and deploying models using AI and GenAI techniques.
• Experience in using Python (e.g., Pandas, NLTK, Scikit-learn, Keras etc.), common LLM development frameworks (e.g., Langchain, Semantic Kernel), Relational storage (SQL), Non-relational storage (No
  
  SQL).

• Experience with fraud detection, risk analytics, or financial crime prevention preferred
• Advanced Graph Analytics:
  Experience implementing graph-based fraud rings detection, money laundering networks, and provider relationship analysis
• Proven problem-solving abilities, including conducting root cause analysis to address specific business inquiries and identify opportunities for enhancement.
• Excellent communication skills to explain complex topics to diverse audiences.
• Demonstrated expertise in the data analytics life cycle, encompassing problem framing, data collection, data cleansing, insights generation, reporting, and communication.
• Skilled in machine learning modeling life cycle, including exploratory data analysis, data cleansing, feature engineering, model building, deployment, and monitoring.

• Experience in developing and deploying models in cloud-based environments, specifically Microsoft Azure, and Databricks, following MLOps best practices.
• Experience with Git Version Control, Unit/Integration/End-to-End Testing, CI/CD, release management, etc.

• We’ll empower you to learn and grow the career you want.
• We’ll recognize and support you in a flexible environment where well-being and inclusion are more than just words.
• As part of our global team, we’ll support you in shaping the future you want to see.

#LI-Hybrid

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