Caremark LLC, a CVS Health company, is hiring for the following role in Woonsocket, RI: Lead 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, Data Science, Statistics, Mathematics, Analytics, Engineering, or a related field and two (2) years of experience in the job offered or related occupation. Requires two (2) years of experience in each of the following: R, Python, or SQL; SAS or SQL programming languages; Machine learning, statistical analysis, and predictive modeling; Data analytics on large data sets in healthcare, business, or retail sector; Marketing analytics, clinical trials, or health research; Designing data models and solutions for analytical and reporting use cases; Big data implementation; Machine learning algorithms; Feature engineering, model training, hyperparameter tuning, distributed model training, and supervised and unsupervised learning implementation; and Quantitative analysis techniques, including clustering, regression, and pattern recognition.
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