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Develop and implement analytics applications and models to transform data into meaningful information. 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.
Job Responsibility:
Develop and implement analytics applications and models to transform data into meaningful information
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
Deliver predictive models that inform solutions for in-house teams and customer engagement across critical journeys
Utilize data-oriented programming languages and visualization software to explore, analyze, and interpret large volumes of data
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
Requirements:
Master’s degree (or foreign equivalent) in Computer Science, Data Science, Statistics, Mathematics, Business Analytics, or a related field
2 years of experience in the job offered or related occupation
GIT or Dev Ops
Programming in R, Hadoop, Python, or SQL
REST, SOAP, or Web Service APIs
Oracle
JIRA, Rally or Confluence
Visualization tools, including PowerBI or Tableau
Machine learning, statistical analysis, and predictive modeling
Relational database concepts
Extract/Transform /Load (ETL) processes
Designing data models and solutions for analytical and reporting use cases
Quantitative analysis techniques, including clustering, regression, and pattern recognition
Designing data architectures, including data pipelines, distributed computing engines, and machine learning infrastructure design
Developing and deploying predictive models or ML systems in a cloud environment (GCP, AWS, or Azure)
End-to-end data science lifecycle/workflow
Agile productivity tools and leading incremental software releases
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