Data Scientist

CVS Health•New York, NY

4d•$109,491 - $173,040•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 New York, NY: 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, Engineering, Statistics, Mathematics, Analytics, or a related field and two (2) years of experience in the job offered or a related occupation.
Two (2) years of experience supporting large data, analytics, and technology modernization initiatives.
Two (2) years of experience in product management in a large, matrixed organization.
Two (2) years of experience in end-to-end data science lifecycle workflow.
Two (2) years of experience developing data science products.
Two (2) years of experience with quantitative analysis techniques, including clustering, regression, and pattern recognition.
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 machine learning algorithms.
Two (2) years of experience designing data models and solutions for analytical and reporting use cases.
Two (2) years of experience with Extract/Transform/Load (ETL) processes.
Two (2) years of experience in marketing or data analytics on large data sets in the healthcare, business, or retail sector.
Two (2) years of experience contributing to large scale applications development, data science, or data analytics projects.
Two (2) years of experience with relational database concepts.
Two (2) years of experience writing application code and deploying to production.
Two (2) years of experience with predictive modeling, and statistical analysis.
Two (2) years of experience with Machine Learning and NLP (Scikit-Learn, SpaCity, Pytorch, or Spark NLP).
Two (2) years of experience with R, Python, MySQL, or SQL.
Two (2) years of experience with Google Cloud Platform (GCP).
Two (2) years of experience with CI/CD.

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.