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Functional Title: Fraud Data Analyst
Job Title: Data Analyst III
Agency: Health & Human Services Comm
Department: Data and Technology
Posting Number: 4284
Closing Date: 09/18/2025
Posting Audience: Internal and External
Occupational Category: Computer and Mathematical
Salary Group: TEXAS-B-22
Salary Range: $4,801.16 - $6,281.00
Shift: Day
Additional Shift: Telework: Eligible for Telework
Travel: Up to 5%
Regular/Temporary: Regular
Full Time/Part Time: Full time
FLSA Exempt/Non-Exempt: Exempt
Facility Location: Job Location City: AUSTIN
Job Location Address: 4601 W GUADALUPE ST
Other Locations: Austin; Abilene; Amarillo; Arlington; Beaumont; Brenham; Bryan; Corpus Christi; Edinburg; El Paso; Fort Worth; Grand Prairie; Houston; Jacksonville; Lockhart; Lubbock; Marble Falls; Mexia; Nacogdoches; Port Arthur; San Angelo; San Antonio; Tyler; Waco; Wichita Falls
MOS Codes: 0231,0239,0271,0291,2652,2659,6046,6049,8051,8825,8846,8848,8850,14N,14NX,15AX,16KX,181X,182X,183X
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Brief Job Description: The Texas Health and Human Services (HHS) Office of Inspector General (OIG) seeks a motivated and talented Fraud Data Analyst (Data Analyst III) to join the Fraud Analytics unit within the OIG's Data Reviews Division.
The OIG's mission is to protect public resources and improve the health, safety, and well-being of Texans by preventing, detecting, auditing, inspecting, reviewing, and investigating fraud, waste, and abuse in the provision and delivery of all state health and human services and enforcing state law related to the provision of those services. The OIG's Data Reviews Division develops OIG initiatives through research and innovation, employing tools and processes that leverage the power of data to increase effectiveness and efficiency.
The Fraud Analytics unit plays a critical role in the OIG's mission by developing and implementing algorithms, artificial intelligence/machine learning (AI/ML) models, and advanced analytics to detect fraud, waste, and abuse, identify and respond to risks, and to generate insights and actionable leads that support investigations, audits, inspections, and reviews in state health and human services programs such as Medicaid, the Children's Health Insurance Program (CHIP), and Supplemental Nutrition Assistance Program (SNAP) and Temporary Assistance for Needy Families (TANF).
The Fraud Data Analyst will be responsible for performing moderately complex (journey-level) data analytics, data research, and data reporting using novice/intermediate level programming to query, prepare, and analyze large, complex health and human services related datasets and to develop algorithms and models that identify data trends, patterns, and anomalies indicative of potential fraud, waste, and abuse. This position prepares written methodology documents; communicates data analytics findings through data visualizations, presentations, and reports; and tests and evaluates new tools, methods, and techniques.
This position reports to the Director of Fraud Analytics and works under general supervision, with moderate latitude for the use of initiative and independent judgment. This position will actively collaborate with Fraud Analytics team members, internal OIG units, and external entities.
Essential Job Functions (EJFs): Attends work on a regular and predictable schedule in accordance with agency leave policy and performs duties as assigned:
1. Plans and conducts data analytics on large, complex datasets using Python to identify risk and detect potential fraud, waste, and abuse in state health and human services programs, with a primary focus on Medicaid. Uses SQL to query databases. Maintains version control and collaborates on code development using Git. Interprets and explains identified data trends, patterns, and anomalies. Develops, implements, and assesses the validity of existing and emerging analytical and statistical tools, methods, and techniques. Applies advanced analytical and statistical methods and techniques to known and new data sources. (40%)
2. Proactively conducts in-depth research on emerging and historical fraud, waste, and abuse schemes and detection methods by analyzing industry reports, policy documents, enforcement actions, and other relevant information sources and translates research findings into actionable insights to support the development of algorithms to detect fraud, waste, and abuse. Collaborates with subject matter experts across internal OIG units and with external entities, including but not limited to: Fraud Analytics team members, OIG program area customers, the OIG executive team, and contractors/vendors, to refine and validate analytic approaches and findings. (35%)
3. Effectively communicates data analytics approaches, findings, and related recommendations to stakeholders both verbally and in writing, including written methodology documents and detailed reporting of findings through data packages, data visualizations, presentations, and reports. Performs other duties necessary to achieve the mission of the Office of Inspector General. Statewide travel of about 5% may be required. (25%)
Knowledge, Skills, and Abilities (KSAs): 1. Knowledge of analytical and statistical tools, methods, and techniques; of data models and querying relational databases; of data wrangling, data mining and data visualization techniques; and of record keeping, including security procedures for handling, protecting, and distributing confidential data.
2. Novice/intermediate programming skill in Python. Novice/intermediate skill in SQL to query databases. Novice/intermediate skill in using Git for version control. Skill in Excel and PowerPoint.
3. Skill in cleaning, preparing, querying, and analyzing large, complex datasets to discover trends and patterns and deliver meaningful insights.
4. Skill in data visualization to present data analytics results and findings.
5. Skill in critical thinking, analyzing problems, and devising effective solutions.
6. Verbal and written communication skills essential to effectively interacting with OIG customers, leadership, and team members.
7. Ability to compile, review, analyze, manage, and manipulate large, complex data sets.
8. Ability to effectively communicate complex data findings and analytical methodologies, both verbally and in writing, to non-technical audiences.
9. Ability to work in teams that may cross departments and/or agencies.
10. Ability to work independently, exercise independent judgment, prioritize tasks, and manage multiple projects/assignments/responsibilities in a fast-paced environment under time constraints.
Registrations, Licensure Requirements or Certifications: N/A
Initial Screening Criteria: Experience in Python for large-scale data preparation and analysis preferred. Experience using SQL to query databases preferred. Experience with Git for version control and collaborative development preferred.
Experience in data analytics, data science, data research, and data reporting work preferred.
Experience in the healthcare domain preferred.
Graduation from an accredited four-year college or university with major coursework in data science, business analytics, healthcare analytics, computer science, computer information systems, management information systems, accounting, finance, mathematics, statistics, economics, or a related field.
Additional Information: - A copy of college transcript will be required.
- The OIG is responsible for preventing, detecting, auditing, inspecting, reviewing, and investigating fraud, waste, and abuse in the provision of HHS in Medicaid and other HHS programs. Potential employees of OIG are subject to criminal background checks in accordance with the HHS Human Resources policy. Selected applicants must complete a national fingerprint-based criminal background check through Texas Department of Public Safety (DPS) and Federal Bureau of Investigation (FBI) to determine if they have criminal history record information that constitutes a bar to employment.
- Any employment offer is contingent upon available budgeted funds. The offered salary will be determined in accordance with budgetary limits and the requirements of HHSC Human Resources Manual.
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