Forecasting Actuary II — Health Policy Analytics (Hybrid)

Texas Health and Human Services; Austin (TX); Hybrid; USD 67,000 - 85,000; Full time

Benefits offered by this job

Telework eligible

Hybrid schedule (80% telework)

Job summary

Texas Health and Human Services Commission is seeking an Actuary II (Forecaster) in Austin to develop complex forecasting models for Medicaid, CHIP, TANF, and related programs. You will analyze historical data, maintain forecasting data integrity, and deliver monthly/quarterly reports to leadership in a hybrid work setting.

The role emphasizes statistical analysis, data quality, and cross-functional collaboration with program staff; prior forecasting experience is preferred and a recent graduate

Qualifications

• Graduation from a 4-year college with major coursework in mathematics/statistics/economics or related field.
• Experience with data analysis tools.
• Experience with forecasting methods and their application to policy planning and budgeting.

Responsibilities

• Develop complex statistical models (time series) using SAS or similar software to forecast HHSC programs.
• Maintain and update spreadsheets and identify opportunities for improvement.
• Pull and analyze historical data monthly to identify anomalies and ensure forecast validity.
• Provide reports to management and legislators on model performance and trends.
• Perform ad-hoc queries and simulations as requested.

Skills

Analytical skills

Statistical software (SAS/R)

Excel

Attention to detail

Communication & collaboration

Education

Bachelor's degree in mathematics/statistics/economics or related field

Tools

SAS

R

SQL

Power BI

Job description

Texas Health and Human Services Commission is seeking an Actuary II (Forecaster) in Austin to develop complex forecasting models for Medicaid, CHIP, TANF, and related programs. You will analyze historical data, maintain forecasting data integrity, and deliver monthly/quarterly reports to leadership in a hybrid work setting.

The role emphasizes statistical analysis, data quality, and cross-functional collaboration with program staff; prior forecasting experience is preferred and a recent graduate