Healthcare Fraud Analytics Data Jobs

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Data Analyst 3

State of New York - Health, Department of Albany, NY

Albany, NY

Today

$112k - $138k/yr Full time Artificial Intelligence Cloud Data Analysis

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Job Description

Lead data analytics and AI/ML development for Medicaid program integrity using cloud graph databases, Python ETL, and fraud detection models.

Responsibilities

Lead and direct unit activities, including establishing goals, priorities, and operational procedures
Provide technical guidance and mentorship to staff across graph databases, Python, AI/ML, and cloud analytics
Design and maintain cloud-based graph database architectures to integrate provider, claims, and encounter data
Define data models and schemas to identify provider networks, referral patterns, and billing relationships linked to FWA
Develop and implement AI/ML models for FWA detection, including:
- Anomaly detection
- Predictive risk scoring
- Natural language processing
- Network analysis to uncover collusive fraud schemes
Build and maintain Python-based ETL pipelines and analytical tools to load data into graph databases and Snowflake
Develop scripts for:
- Pattern detection
- Risk scoring
- Data quality analysis
- Automated alerting
Architect scalable and secure cloud infrastructure using Snowflake and graph database platforms for real-time and batch fraud detection processing
Research and evaluate emerging AI technologies for Medicaid FWA applicability
Develop proof-of-concept solutions and present findings and recommendations to management
Collaborate with program integrity units, program areas, and technical teams to translate investigative and program integrity needs into technical specifications
Develop and maintain FWA dashboards, reporting tools, and alert systems
Provide management with analytics, risk assessments, visualizations, and actionable intelligence to support program integrity decisions
Maintain documentation for database schemas, AI/ML models, algorithms, data pipelines, and cloud architecture
Establish coding standards and best practices for the unit
Review and resolve data quality issues, system deficiencies, and integration challenges impacting FWA detection capabilities
Perform the full range of supervisory responsibilities over Data Analysts and technical staff within the unit

Requirements

Minimum experience: 9 years
Experience: nine years of experience in data visualization, and statistical models and data mining tools
Substitution: an associate’s degree may substitute for two years of general experience; or a bachelor’s degree may substitute for four years of general experience
Education substitution: a master’s degree in data or computer science, informatics, economics, statistics, mathematics, or a related field may substitute for one year of general experience; or a Ph.D. in these fields may substitute for two years of general experience

Preferred Qualifications

Proficiency in Python for data analysis, automation, ETL development, and machine learning applications
Experience with Medicaid or Medicare claims, encounter, and provider data systems, including knowledge of FWA detection methodologies and healthcare coding systems
Supervisory experience leading technical or data analytics teams, with a track record of standing up new programs or units
Familiarity with HIPAA, healthcare data privacy requirements, and state or federal government environments

Technologies

Graph databases
Python
AI/ML
Cloud analytics
Snowflake
Natural language processing
Medicaid
Medicare
HIPAA

Benefits

Holiday & Paid Time Off
Public Service Loan Forgiveness (PSLF)
Pension from New York State Employees’ Retirement System
Shift & Geographic pay differentials
Affordable Health Care options
Family dental and vision benefits at no additional cost
NY Deferred Compensation plan
Access to NY 529 and NY ABLE College Savings Programs, and U.S. Savings Bonds

Location: Albany, NY (onsite) | Salary: USD 111,708 - 137,590 per yearly

Additional notes: Candidates must provide accurate and clear descriptions of their experience demonstrating how they meet the minimum qualifications. Omissions and vagueness cannot be interpreted in the candidate’s favor.

When reviewing qualifications, omissions and vagueness cannot be interpreted in the candidate’s favor.

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