Data Scientist

CVS Health•Woonsocket, RI

4d•$141,565 - $144,200•Remote

About The Position

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. Aetna Resources LLC, a CVS Health company, is hiring for the following role in Woonsocket, RI: Data Scientist to formulate and apply mathematical and computational modeling methods towards data extraction and manipulation. Duties include: design and develop data solutions using industry leading tools, technologies and best practices to profile data and develop efficient ingestion by sourcing data from PBM, Specialty, Retail, and/or HealthCare business; develop advanced algorithms and statistical predictive models to evaluate scenarios and provide usable information on health metrics and potential future outcomes; extract and manipulate data from multiple large data sources and deliver predictive models that inform solutions for in-house teams (i.e. pharmacy pricing, medical costs, risk scores, onboarding) and customer engagement across critical journeys (i.e. calories, heart rate, breast cancer, maternity); utilize strong programming skills to explore, analyze, and interpret large volumes of data in various forms and solve complex business problems; support deployment of insights across multiple channels and work on mathematical analysis methods, machine learning, and statistical analyses. Telecommuting available. Multiple Positions.

Requirements

Bachelor’s degree (or foreign equivalent) in Computer Engineering, Electronics Engineering, Statistics, Mathematics or a related field and five (5) years of progressive, post-baccalaureate experience in the job offered or a related occupation.
Five (5) years of experience interpreting large datasets using statistical methods, programming skills, and machine learning techniques using statistical models like WEIBULL, PARETO, BOX PLOT, CONTRL CHARTS, REGRESSION, CORRELATION, ANOVA using SAS and Python.
Five (5) years of experience in development using procedures in Base SAS like PROC SQL, REPORT, SHEWART, GREPLAY, TEMPLATE, TABULATE, FORECAST, TRANSPOSE, GREPLAY, MEANS, SORT, BOXPLOT, GPLOT, GSLIDE and SAS ODS.
Five (5) years of experience in designing and developing relational and multi-dimensional reports, KPI's, dashboards using SAS Base, SAS Macros and SAS Graph, SAS Cube.
Five (5) years of experience in SAS report development and support project.
Five (5) years of experience in integration of MSRS server with dot net front end application using web services.
Five (5) years of experience in creating packages in FM and designing reports in COGNOS Report Studio using Oracle database.

Responsibilities

Formulate and apply mathematical and computational modeling methods towards data extraction and manipulation.
Design and develop data solutions using industry leading tools, technologies and best practices to profile data and develop efficient ingestion by sourcing data from PBM, Specialty, Retail, and/or HealthCare business.
Develop advanced algorithms and statistical predictive models to evaluate scenarios and provide usable information on health metrics and potential future outcomes.
Extract and manipulate data from multiple large data sources and deliver predictive models that inform solutions for in-house teams (i.e. pharmacy pricing, medical costs, risk scores, onboarding) and customer engagement across critical journeys (i.e. calories, heart rate, breast cancer, maternity).
Utilize strong programming skills to explore, analyze, and interpret large volumes of data in various forms and solve complex business problems.
Support deployment of insights across multiple channels and work on mathematical analysis methods, machine learning, and statistical analyses.