Senior Data Scientist

Vizient•Chicago, IL

14d•$142,968 - $179,000•Hybrid

About The Position

When you’re the best, we’re the best. We instill an environment where employees feel engaged, satisfied and able to contribute their unique skills and talents while living and working as their authentic selves. We provide extensive opportunities for personal and professional development, building both employee competence and organizational capability to fuel exceptional performance through an inclusive environment both now and in the future. DUTIES: Provide statistically and methodologically sound analytic solutions to ensure validity regarding hypothesis evaluation, study design/methodology development and findings to drive improvements in clinical, operational, and economic outcomes for member hospitals. Develop computer programs using Python and perform necessary manipulations to import complex external data into Python or export output to other databases/files. Work with technology team and other stakeholders on setting reasonable expectations regarding data formatting and turn-around times. Provide computationally complex analysis using statistical software for leadership team to identify critical issues/questions around clinical performance improvement data. Generate summary tables, data listings, graphs and derived datasets as specified in statistical analysis plan. Generate outputs and reports to support data science and methodological workflows. Conduct statistical inference analysis and testing to validate develop and evaluate data driven hypotheses, accuracy and results. Develop data science solutions and insights across multiple projects, and single or multiple domains, with minimal guidance. Communicate findings from exploratory and predictive data analysis to administrative and clinical leaders for multiple projects. Demonstrate skills in statistical design, efficiency, and sophisticated problem solving for multiple projects. Provide guidance to other data scientists in the design of data science insights. Demonstrate expertise in core data assessment steps (e.g., data discovery, structuring, cleaning, enrichment, and validation) and publish insights across multiple data sets. Independently leverage machine learning, natural language processing, or other statistical approaches to create, trouble shoot, and implement single platform solutions. Make autonomous decisions to operationalize data modeling, exploratory data analysis, inferential statistics machine learning model development, project deployment, and visualization based on specified project goals or design objectives. Demonstrate expertise in relevant core programming tools (e.g., R, Python, SAS, SQL, MATLAB, JAVA). Utilizes expertise with relevant data platforms (e.g., Tableau, Apache Spark, Azure Data Bricks, Hadoop). Execute low to medium complex (e.g., merging data sets from disparate sources, normalizing and standardizing highly irregular or inconsistent data) technical code revisions independently or for select portion of projects. Apply solutions-oriented mindset to project-specific goals and objectives. Make autonomous project-specific decisions supporting data science deliverables within.

Requirements

This position requires a Master’s degree in Applied Data Science or a directly related field of study, plus the following experience: 5 years of experience to include all of the following areas: (1) building SAS/Python based ETL Pipelines and Analytic Reports; (2) reengineering collections and forbearance data architecture to facilitate loss mitigation reporting to leadership team and stakeholders; (3) building scalable applications and data pipelines for data analytics and internal risk assessment teams; (4) performing statistical analysis and data validations on adhoc basis for various business user requirements; (5) building datasets and generating analytic reports for different business process analysis and validations; (6) utilizing SAS Procedures including Proc SQL and Proc Sort for analyzing and reporting data; (7) building new ETL Pipelines and enhancing existing pipelines for customizing and streamlining data from multiple sources using shell scripting, SAS and Python; and (8) Data Analysis and Data Mapping for ETL process using SAS, Python, and shell scripting.
Must also have each of the following: (1) 2 years experience in developing computer programs using Python and performing the necessary manipulations to import complex external data into Python or export output to other databases/files; (2) 2 years experience in working with the technology team and other stakeholders on setting reasonable expectations regarding data formatting and turn-around times; (3) 2 years experience in generating summary tables, data listings, graphs and derived datasets as specified in the statistical analysis plan; (4) 2 years experience in generating outputs and reports to support data science and methodological workflows; and (5) 2 years experience in building machine learning pipelines and/or AI solutions.
Experience may be gained concurrently.
Full-time, position located in Chicago, IL.
Hybrid position.
Must reside within commuting distance from the employer’s Chicago, IL office.
Various unanticipated locations throughout the U.S.

Responsibilities

Provide statistically and methodologically sound analytic solutions to ensure validity regarding hypothesis evaluation, study design/methodology development and findings to drive improvements in clinical, operational, and economic outcomes for member hospitals.
Develop computer programs using Python and perform necessary manipulations to import complex external data into Python or export output to other databases/files.
Work with technology team and other stakeholders on setting reasonable expectations regarding data formatting and turn-around times.
Provide computationally complex analysis using statistical software for leadership team to identify critical issues/questions around clinical performance improvement data.
Generate summary tables, data listings, graphs and derived datasets as specified in statistical analysis plan.
Generate outputs and reports to support data science and methodological workflows.
Conduct statistical inference analysis and testing to validate develop and evaluate data driven hypotheses, accuracy and results.
Develop data science solutions and insights across multiple projects, and single or multiple domains, with minimal guidance.
Communicate findings from exploratory and predictive data analysis to administrative and clinical leaders for multiple projects.
Demonstrate skills in statistical design, efficiency, and sophisticated problem solving for multiple projects.
Provide guidance to other data scientists in the design of data science insights.
Demonstrate expertise in core data assessment steps (e.g., data discovery, structuring, cleaning, enrichment, and validation) and publish insights across multiple data sets.
Independently leverage machine learning, natural language processing, or other statistical approaches to create, trouble shoot, and implement single platform solutions.
Make autonomous decisions to operationalize data modeling, exploratory data analysis, inferential statistics machine learning model development, project deployment, and visualization based on specified project goals or design objectives.
Demonstrate expertise in relevant core programming tools (e.g., R, Python, SAS, SQL, MATLAB, JAVA).
Utilizes expertise with relevant data platforms (e.g., Tableau, Apache Spark, Azure Data Bricks, Hadoop).
Execute low to medium complex (e.g., merging data sets from disparate sources, normalizing and standardizing highly irregular or inconsistent data) technical code revisions independently or for select portion of projects.
Apply solutions-oriented mindset to project-specific goals and objectives.
Make autonomous project-specific decisions supporting data science deliverables within.