Data Scientist Health Fraud Analytics

John Hancock is seeking a highly technical and creative Data Scientist to join our Long Term Care Insurance fraud analytics and AI team! This role combines deep healthcare domain expertise with advanced analytics to protect our policyholders and company from fraudulent claims while ensuring legitimate claims are processed efficiently. Youll work at the intersection of insurance healthcare delivery and data science to build sophisticated detection systems for one of the most complex fraud landscapes in financial services!

Position Responsibilities:
Time Series & Longitudinal Health Analytics

• Develop predictive models analyzing patient health trajectories over multi-year periods to identify statistically improbable recovery patterns or care progression anomalies
• Build longitudinal cohort analysis frameworks to detect unusual claim patterns across similar patient populations
• Build temporal feature engineering pipelines that capture disease progression treatment response patterns and care critical issue trends
• Design early warning systems for claims that deviate from expected long-term care utilization patterns

Behavioral Analytics & Sequential Pattern Mining

• Analyze provider billing sequences to identify unusual patterns in care delivery service combinations or billing timing
• Develop session-based analysis of claimant interactions with care providers to detect orchestrated fraud schemes
• Build behavioral profiles of legitimate vs. fraudulent claim submission patterns
• Develop anomaly identification systems for provider practice trends and claimant care utilization behaviors

Healthcare Claims Analytics & Medical Coding

• Deep analysis of long-term care service codes daily benefit triggers and activities of daily living assessments
• Develop expertise in long-term care assessment tools (e.g. nursing home assessments home care evaluations)
• Build validation systems for medical necessity determinations and benefit eligibility criteria
• Create automated systems to detect inconsistencies between medical documentation and claimed care needs

Provider Network & Credentialing Analytics

• Analyze provider networks for suspicious patterns in licensing credentialing and service delivery capabilities
• Develop risk scoring systems for care providers based on claim patterns licensing history and network relationships
• Build systems to validate provider capacity claims against actual service delivery patterns
• Develop monitoring mechanisms for provider connections and potential collusion indicators

Required Qualifications:
Education and Experience:

• PhD or MS Bioinformatics Computer Science Clinical Research or related quantitative field
• 5 years of experience in healthcare analytics or related field

Technical Skills:

• Expert-level proficiency in a coding language such as C C Python R
• Expert level proficiency in SQL
• Experience with time series analysis survival analysis and longitudinal data modeling
• Proficiency with graph analytics sequence mining network analysis and behavioral pattern recognition

Domain Expertise:

• Knowledge of healthcare delivery systems
• Knowledge of medical coding systems (ICD-10 CPT HCPCS) and healthcare reimbursement models
• Familiarity with long-term care insurance products benefit structures and claims processes
• Understanding of healthcare provider credentialing and licensing requirements
• Understanding healthcare fraud typologies and detection methodologies

Analytical Skills:

• Advanced statistical modeling and machine learning expertise
• Experience with unsupervised learning anomaly detection and imbalanced classification problems
• Strong feature engineering capabilities particularly for temporal and sequential data
• Experience with model validation performance monitoring and regulatory compliance frameworks

When you join our team:

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

#LI-Hybrid