p>Requirements: Master's degree (or foreign equivalent) in Computer Science, Data Science, Statistics, Mathematics, Analytics, or a related field and completion of a university-level course, research project, internship, thesis, or six (6) months of experience in each of the following: - Big Data, Smart (Semantic) Data, data modeling, and data visualization and applicability to health, education, public safety, or other public and private sectors;
- Development of subroutines/codes/scripts;
- Exploratory data analysis, including pre-processing, cleaning, and preliminary examination;
- Descriptive analytics concepts, including sampling and statistical inferences, in the public and private sector;
- Predictive analysis techniques, including regression, forecasting, and simulations;
- Visual and graphical representation of data;
- Quantitative decision-making using data received from multiple sources;
- Machine learning, including developing algorithms, data mining, pattern recognition, and supervised/unsupervised learning;
- Programming in R, Octave, and Python; and. 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.