Compiles, analyzes, and interprets healthcare clinical and utilization data for the Data Science Team. Provides statistical analysis design plans and leverages relevant data to support strategic business decisions. Conducts statistical analysis, forecasting, predictive modeling, and simulations according to the statistical analysis plan and develops reports, tables, visualizations and other supporting documentation to disseminate findings to business. Deploys forecasting applications or predictive models through various mechanisms.
Conducts end-to-end analysis that includes data gathering from internal and external sources, specifying requirements, processing, compiling, validating and modelling data; ensures deliverables are met and prepares presentations.
Ensures data quality throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, and transformation.
Interacts cross-functionally with a wide variety of teams, including end-users, to ensure that deployed algorithms are serving business needs.
Solves non-routine, inter-disciplinary analysis problems by applying advanced analytical methods as needed.
Communicates data and results with meaningful visualizations and tables.
Works with clinical operations, business owners, or IT to understand how VNSNY applications enter and commit data records into database systems.
Responds to business requests with descriptive reports in a timely manner. Uses ad-hoc requests to gain knowledge of business and data systems.
Makes business recommendations (e.g. cost-benefit, forecasting, experiment analysis, inference) to stakeholders through visual displays of quantitative information.
Ensures the accuracy of work and the meaningful interpretation of the statistical inference.
Develops and prepares analytic plans, including statistical design and methodology; executes analysis as specified, creates final reports, and presents findings to business management.
Participates in special projects and performs other duties, as needed.
Education: Master’s Degree in Statistics, Biostatistics, Mathematics, Data Science, Econometrics, Epidemiology or other statistics related degree required.
Experience: Minimum of two years of data analysis experience required. Ability to apply statistical methods to data, draw conclusions from data, and make recommendations required. Experience using statistical software (e.g. R, Python, Julia, MATLAB, SAS, STATA) required. Effective oral, written and interpersonal communication skills required. Knowledge of relational databases and programming experience in SQL preferred. Experience with R-Shiny and business intelligence applications preferred. Experience with medical claims and health assessment data (e.g. OASIS, UAS-NY) preferred. Knowledge of Medicare and Medicaid payment policy and alternative payment models (e.g. BPCI, PDGM Value Based Payments, dual-risk models, Hospice Final Rule) preferred. Hands on experience building and deploying predictive models (API, database ETL, R/Python application integration to BI or ETL tools), specifically in a health care setting, preferred. Application of quasi-experimental methods to health care data preferred.