Data Scientist

CVS Health•New York, NY

4d•$109,491 - $222,480•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. Position Summary: Caremark, LLC., a CVS Health company, is hiring for the following role in New York, NY: Data Scientist to develop and implement analytics applications and models to transform data into meaningful information. 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, predict outcomes, and provide usable information on health metrics and potential future outcomes; utilize data mining, data modeling, natural language processing, and machine learning to 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 data-oriented programming languages and visualization software to explore, analyze, and interpret large volumes of data in various forms and solve complex business problems; visualize and interpret data and create reports, manipulate data using statistical software, and compare models using statistical performance metrics; and support deployment of insights across multiple channels using analysis methods, machine learning, and statistical analyses. Telecommuting available. Multiple positions.

Requirements

Master’s degree (or foreign equivalent) in Computer Science, Computer Engineering, Data Science, Statistics, Mathematics, Analytics, or a related field and two (2) years of experience in the job offered or related occupation.
Programming in R, Hadoop, or Python
SAS or SQL programming languages
Tools to automate CI/CD pipelines: Jenkins, GIT, or Control-M
Spark, Airflow, Kafka, Hbase, Pig, or NoSQL
REST, SOAP, or Web Service APIs
Oracle, Redis, or Cassandra
Software Development Life Cycle (SDLC) and best practices
Writing application code and deploying to production
Large scale data applications development, data science, or data analytics projects
Developing backend services, performing code reviews, and collaborating with peers on software development solutions
Relational database concepts

Responsibilities

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, predict outcomes, and provide usable information on health metrics and potential future outcomes
Utilize data mining, data modeling, natural language processing, and machine learning to 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 data-oriented programming languages and visualization software to explore, analyze, and interpret large volumes of data in various forms and solve complex business problems
Visualize and interpret data and create reports, manipulate data using statistical software, and compare models using statistical performance metrics
Support deployment of insights across multiple channels using analysis methods, machine learning, and statistical analyses