Leads the analysis of complex and unstructured data sets using advanced statistical methods for use in data driven decision making
Responsible for leading cross-functional teams and providing in-depth data insights for complex business problems approached with advanced analytic techniques to collect, explore, and extract insights from structured and unstructured data
Manages engagement with internal customers on small and medium sized projects
Provides findings and analysis for use in decision making
Develops custom data models and algorithms to apply to data sets
Develops and tests hypotheses and communicates findings in clear, precise and actionable manner to project and leadership teams
Works closely with teams to identify, understand, and resolve data issues and improve efficiency, productivity and scalability of data processes

Bachelor’s Degree in science, engineering, computer science, mathematics, statistics, or related STEM field required
Master’s Degree in Data Science preferred
Seven (7) years of experience in data science is required
Professional experience in hospital setting, medical informatics, healthcare information technology/finance/revenue cycle data management, or Electronic Health Record (EHR) data management is preferred
Business analytical skills (process flows, procedures, spreadsheets, modeling, etc.)
Technical expertise, mathematical skills and good understanding of design and architecture principles are required
Possesses deep understanding of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks
Proficient understanding of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications
Advanced level knowledge of the data science project life cycle
Proficient programming skills in addition to a working knowledge and experience of statistical analysis tools
Strong analytical skills and ability to understand and interpret results based on advanced statistical techniques

Lead Data Scientist

Key skills