Data Analyst 3
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.