Sr Data Scientist

CVS Health•New York, NY

19h•$111,240 - $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: Sr 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

Master’s degree (or foreign equivalent) in Computer Science, Data Science, Operations Research, Statistics, Mathematics, Analytics, or a related field
Two (2) years of experience in R, Python, or SQL
Two (2) years of experience with Visualization tools, including PowerBI or Tableau
Two (2) years of experience with “Big data” platforms including Hadoop (Azure, GCP, or AWS) and Spark, Airflow, Kafka, Hbase, Pig, or NoSQL
Two (2) years of experience translating analytical problems into structured programs using PySpark, Scala, PL/SQL or Teradata BTEQ
Two (2) years of experience with Machine Learning or NLP (Scikit-Learn, SpaCity, Pytorch, or Spark NLP)
Two (2) years of experience with Data analytics on large data sets in healthcare, business, or retail sector
Two (2) years of experience with Marketing analytics, clinical trials, or health research
Two (2) years of experience with Containerized services, storage, and workspace management
Two (2) years of experience with Feature engineering, model training, hyperparameter tuning, distributed model training, and supervised and unsupervised learning implementation
Two (2) years of experience with Qualitative analysis techniques, including clustering, regression, and pattern recognition

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.