EchoStar is looking to solve business and technical problems related to causal analysis, statistics, and economic modeling to drive innovation in the wireless and technology industries.
Requirements
- Strong Python skills, specifically with libraries for data analysis (pandas, numpy, scikit-learn, etc.)
- Solid understanding of statistical concepts (regression, hypothesis testing, confidence intervals)
- Familiarity with Econometrics or Causal Inference concepts (e.g., A/B testing, Difference-in-Differences) is a huge plus, but not required
Responsibilities
- Assist in analyzing the results of marketing and pricing changes using statistical methods (e.g., looking at pre-trends and control groups) to determine true lift
- Help validate our 'Causal Engine' by running diagnostic checks—essentially 'stress testing' our math to ensure our AI isn't hallucinating insights
- Support the development of 'Uplift Cards'—standardized reports that translate statistical data into dollar values (Net Present Value)
- Prototype Python scripts to calculate basic financial metrics like 'Payback Period' or 'Lifetime Value' based on experimental data
- Analyze historical data to help identify patterns in customer retention and churn
Other
- GPA 3.3 or above
- Currently enrolled in an undergraduate or graduate program, in a related field of study
- Must have 60 credit hours completed by May 2026
- Successful completion of a pre-employment screen including a reference check, criminal background check, and possible drug test
- Visa sponsorship not available for this role
