SAS Data Scientist I: Harmonize & Visualize Healthcare Data

DataJobs; Chicago (IL); Hybrid; USD 90,000 - 104,000; Full time

Benefits offered by this job

Health insurance

Retirement plan

Paid time off

Education benefits

Job summary

NORC at the University of Chicago is seeking a Data Scientist I to support hospital discharge data processing across states and harmonize, standardize, and package files into a uniform format. This hybrid role covers end-to-end data work, including importing, cleaning, validating, transforming data, and producing reports and visualizations for project teams.

You will collect, clean, transform, validate, and harmonize data from multiple sources, then identify trends, patterns, and data quality

Qualifications

• Bachelor’s degree in quantitative fields (required).
• Advanced degree preferred.
• Proficiency with data management and data processing.

Responsibilities

• Collect, clean, transform, validate, and harmonize data from multiple sources.
• Identify trends and data quality issues; communicate findings to project teams.
• Develop reports, dashboards, tables, charts, and visualizations.
• Create, document, manage, and maintain databases and data dictionaries.
• Support data storage optimization and data management procedures.
• Collaborate with research teams to understand analytical needs.
• Contribute to proposal development and drafting technical sections.

Skills

SAS macros

SQL

Python

R

Tableau

Hadoop

Hive

MapReduce

MS Office

Education

Bachelor’s degree

Advanced degree

Tools

SAS

SQL

Python

R

Tableau

Hadoop

Hive

MapReduce

MS Office

Job description

NORC at the University of Chicago is seeking a Data Scientist I to support hospital discharge data processing across states and harmonize, standardize, and package files into a uniform format. This hybrid role covers end-to-end data work, including importing, cleaning, validating, transforming data, and producing reports and visualizations for project teams.

You will collect, clean, transform, validate, and harmonize data from multiple sources, then identify trends, patterns, and data quality