Senior Epidemiology Data Scientist

University of Chicago•Chicago, IL

2d•$110,000 - $150,000•Hybrid

About The Position

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.

Requirements

College or university degree in related field.
7+ years of work experience in a related job discipline.

Nice To Haves

Master’s degree (MPH, MS) in public health, epidemiology, or health services research.
At least 7-10 years of experience in analyzing large clinical datasets from both public and private health sectors.
At least 7 years of experience working with data from the Epic electronic medical record system.
At least 5-7 years of experience in supporting development of research methods for key healthcare system programs, including those programs designed to improve quality improvement and infectious disease prevention.
At least 5 years of experience in supporting scientific manuscript development.
At least 5-7 years of experience in designing and planning coordination with physician scientists in the healthcare setting.
Demonstrated experience in developing data products for analysis and in liaising between researchers and technical staff.
Prior experience with biomedical data analysis (e.g. biostatistics).
Demonstrated leadership to supervise other statistical team members in the healthcare setting including other statisticians, system reporting specialist, and population health epidemiologists.
Prior leadership in public health epidemiology.
Previous experience with community-based organizations and working with community partners to improve health and wellness in community settings.
Proven ability to lead multidisciplinary teams and manage cross-sector collaborations.
Ability to apply advanced epidemiological principles to infectious disease surveillance, outbreak investigation, and risk factor analysis.
Ability to design and execute spatial statistical models to identify geographic patterns of disease risk at the community level.
Ability to build and validate predictive models to identify communities most at risk for existing and emerging infectious diseases.
Ability to contribute to peer-reviewed publications, grant applications, and technical reports; translate complex findings for clinical, community, and policy audiences.
Ability to manage multiple research projects and timelines concurrently while maintaining scientific rigor and meeting deliverable commitments.
Demonstrated experience using quantitative analysis tools (SPSS, STATA, R, SQL, and SAS).
Advanced knowledge of epidemiology and statistical analysis methodology.

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 development of manuscripts and presentations at national meetings.
Participates in the scientific and scholarly education of learners, including students at the the undergraduate, graduate, and post-doctorate levels.
Leads and develops methods to analyze complex data sets for the purpose of extracting and purposefully using applicable information. Develops and maintains infrastructure that connects medium to large complex data sets.
Provides expertise to staff or faculty members in defining the project and applies principals of data science in manipulation, statistical applications, programming, analysis and modeling.
Recommends process improvements for data calibration between large and complex research and administrative datasets. Implements and may improve upon the established operational protocols for collecting and analyzing information from the University's various internal data systems as well as from external sources.
Leads the design and evaluation of statistical models and reproducible data processing pipelines using expertise of best practices in machine learning and statistical inference. Provides expertise and/or recommends process improvements for high level or complex data-related requests and engages other IT resources as needed. Establishes partnerships with other campus teams to assist faculty with data science related needs.
Performs other related work as needed.