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# TELECOMMUTE Data Scientist

- **Company:** Tanisha Systems Inc
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Data Architecture, Information Engineering, Python (Programming Language), Standard Sql, Business Intelligence Development Studio, Snowflake
- **Published:** September 29, 2026
- **Apply:** https://www.dice.com/job-detail/e2e350d3-d3e4-41ca-83cb-86d3fe9c2bf8

## About the Role

* 5 or more years of analytics or data science experience in healthcare, with direct exposure to healthcare economics, actuarial analytics, or value based care measurement.
 * Strong SQL plus Python or R for analysis and modeling.
 * Working knowledge of medical and pharmacy claims data, including standard code sets and common data quality pitfalls.
 * Demonstrated ability to explain methodology and findings to non technical audiences.
Preferred qualifications

 * Experience with risk adjustment models, quality measure sets, or shared savings and total cost of care programs.
 * Experience building client facing or externally published reporting.
 * Experience working in Snowflake and with modern BI tooling."

## Description

Purpose of the role: Improve the quality, credibility, and usefulness of our client reporting. The role brings a healthcare economics or value based care background so that what we report to clients reflects sound methodology: risk adjustment, attribution, utilization and cost trend, and defensible savings and outcomes measurement., * Design and improve the analytics behind client reporting, including cost of care trend, utilization, program impact, and return on investment.

 * Build and validate measurement methodology such as risk adjustment, cohort construction, attribution logic, and comparison group design.
 * Turn analytic output into reporting that a client audience can read and trust, partnering with client facing teams on narrative and presentation.
 * Document methodology so results can be explained, audited, and reproduced.
 * Partner with data engineering and the data architect on the models and data assets reporting depends on.
 * Investigate anomalies and questions raised by clients or internal stakeholders and close them with evidence.