Senior Epidemiology Data Scientist​

Department BSD PED – Infectious Diseases – Vaccine Center of Excellence/ID Health Disparities

About the Department

​​The section of Pediatric Infectious Disease provides comprehensive inpatient and outpatient care for children suffering from a wide variety of infectious diseases. Infectious disease specialists are experts in diseases caused by bacteria, viruses, fungi, and parasites.

​This position will provide high-level epidemiology and data analysis support for Lilly Cheng-Immergluck’s Lab. Dr. Cheng-Immergluck’s lab undertakes data science and epidemiological research to advance knowledge about prevention of specific infectious diseases, which have a propensity for developing antibiotic resistance, e.g., Staphylococcus aureus and Streptococcus pneumoniae, as well as those pathogens which have contributed to epidemic outbreaks, are more likely to severely infect children, and are often times, vaccine preventable, e.g., RSV, measles, pertussis, etc. The mission of the work of this multi-disciplinary team is to explore the intersection of public health epidemiology and primary prevention care models with the goal to improve health outcomes of populations at greatest risk for vaccine preventable condition. Her lab uses spatial statistical models, geographic information system tools, bioinformatics, and artificial intelligence to predict communities most at risk for existing and emerging infectious diseases. This position will also play a key role in the Vaccine Center of Excellence which advances public health via research, advocacy and education of providers and the community served by UChicago; community based participatory research and engagement are cornerstones of this Center’s work.​

Job Summary

​​The Senior Epidemiology Data Scientist will work in collaboration with Dr. Lilly Cheng-Immergluck’s to design and implement research studies focused on infectious disease prevention. In this capacity, the incumbent plays a key role in the design of research studies and their analysis plans. Responsibilities include overseeing study design, providing input on project protocols, and mentoring more junior research staff. The ideal candidate not only brings extensive experience in statistical analysis and study design for clinical research using data from electronic medical records but also should be able to perform the programming needs required by the project. This individual will also have had experience with scientific writing, which is required for funding applications and manuscripts for scientific and biomedical peer-reviewed journals. The incumbent will be a key contributor across a number of projects in the Immergluck Lab portfolio, with a particular focus on the intersection of clinical and population health data. They will work collaboratively with specialists in clinical data standards, data operations, and technology to bring together clinical and non-clinical data to support research studies to inform prevention of infectious diseases.​

The job leads and provides expertise to the development of programs for data manipulation, statistical applications, programming, analysis and modeling in order to implement projects related to the University's various internal data systems as well as from external sources.

Responsibilities

• ​​Leads development of data products according to specifications determined through collaborative meetings with PIs and co-investigators.
• ​Oversees and organizes data quality control programs and maintains clear provenance of clinical and non-clinical data during the joining and analysis of datasets.
• ​Applies their deep understanding of biostatistical methods to analyze complex and large data sets for the purpose of extracting and using applicable information.
• ​Develops and maintains infrastructure that supports integration of data across sources, e.g., clinical data, biospecimens, and administrative data.
• ​Designs and evaluates statistical models and reproducible data processing pipelines using expertise and best practices in statistical inference. Provides expertise for high-level or complex data-related requests and engages other internal resources as needed.
• ​Partners with collaborating teams at external institutions to support the overall data science needs of projects.
• ​Supports interpretation and visualization of results for manuscripts, presentations, and grant applications.
• ​Contributes substantively to grant applications, including methods, analytic plans, and preliminary data.
• ​Contributes substantively to dev