Data Scientist - Health Fraud Analytics

Manulife

2d•Hybrid

About The Position

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. You'll 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

Requirements

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

Responsibilities

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
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
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
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

Benefits

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.
Manulife/John Hancock offers eligible employees a wide array of customizable benefits, including health, dental, mental health, vision, short- and long-term disability, life and AD&D insurance coverage, adoption/surrogacy and wellness benefits, and employee/family assistance plans.
We also offer eligible employees various retirement savings plans (including pension/401(k) savings plans and a global share ownership plan with employer matching contributions) and financial education and counseling resources.
Our generous paid time off program in the U.S. includes up to 11 paid holidays, 3 personal days, 150 hours of vacation, and 40 hours of sick time (or more where required by law) each year, and we offer the full range of statutory leaves of absence.

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