- Salary
- $93k – $95k
- Location
- Work@Home Georgia, United States of America
- Workplace
- Remote
- Type
- Full-time
- Department
- Education
- Seniority
- Senior
- Source
- Workday
Description
Sagility combines industry-leading technology and transformation-driven BPM services with decades of healthcare domain expertise to help clients draw closer to their members. The company optimizes the entire member/patient experience through service offerings for clinical, case management, member engagement, provider solutions, payment integrity, claims cost containment, and analytics. Sagility has more than 25,000 employees across 5 countries.
TITLE: Senior Data Scientist
LOCATION: Atlanta, GA, and various and unanticipated locations throughout the U.S. (Must be willing to work anywhere in the U.S. as the position may involve relocation to various and unanticipated client site locations; any relocation to be paid by employer pursuant to internal policy.)
DUTIES: Formulate and apply mathematical modeling and other optimizing methods to develop and interpret information that assists clients’ management with strategic decision-making. Evaluate population health management program tracks performance using propensity matching, and analyze baseline and study period of members in test and control groups for clients utilizing Python, SAS, Power BI, and Excel. Implement complex statistical, machine learning, and NLP (natural language processing) models. Apply techniques for analyzing fraud, waste, and abuse case studies to draw relevant insights for the end users. Analyze “lift” in the company’s care management programs, medication adherence, and clinical visit, using a propensity-based methodology to ascertain the impact of operations on critical measures that indicate program benefits. Produce a probabilistic model using business understanding in Home Health and LTC. Build predictive models that improve savings and operational efficiency for both pre and post-payment cycles across various payment types. Design and deliver ML/AI solutions to work on cross-client platforms to identify inappropriate provider behavior and stratify social risk. EOE
REQTS: Must have a Bachelor’s degree or foreign equivalent in Engineering (Any), Data Analytics, or a related field plus five (5) years of experience in the position offered, as a Data Analyst, or a related position. Must have five (5) years of experience with all of the following: Statistical and predictive modeling techniques including machine learning and natural language processing; Data mining and conducting statistical studies and evaluating various program performances; and Programming applications using Python, R, SQL, and SAS to extract and transform data from multiple data sources. Must include three (3) years of experience with all of the following: Data modeling for the healthcare field, including both insurance and providers; and Healthcare insurance, healthcare economics, managing care plans, billing requirements, subscriber and provider responsibilities, utilization management, risk identification and adjustment, and long-term care.
SALARY: $92,901.04 - $95,000.00 per year
HOURS: 40 hours per week, Monday-Friday
Job title:
Job Description:
Formulate and apply mathematical modeling and other optimizing methods to develop and interpret information that assists clients’ management with strategic decision-making. Evaluate population health management program tracks performance using propensity matching, and analyze baseline and study period of members in test and control groups for clients utilizing Python, SAS, Power BI, and Excel. Implement complex statistical, machine learning, and NLP (natural language processing) models. Apply techniques for analyzing fraud, waste, and abuse case studies to draw relevant insights for the end users. Analyze “lift” in the company’s care management programs, medication adherence, and clinical visit, using a propensity-based methodology to ascertain the impact of operations on critical measures that indicate program benefits. Produce a probabilistic model using business understanding in Home Health and LTC. Build predictive models that improve savings and operational efficiency for both pre and post-payment cycles across various payment types. Design and deliver ML/AI solutions to work on cross-client platforms to identify inappropriate provider behavior and stratify social risk.
Location:
