Postdoctoral Research Fellow

Postdoctoral Research Fellow

Position:
Postdoctoral Research Fellow

School:
Harvard T.H. Chan School of Public Health

Department/Area:
Epidemiology

Position

Description:

The Department of Epidemiology at the Harvard T.H. Chan School of Public Health studies the frequency, distribution, and determinants of disease in humans, a fundamental science of public health. In addition to pursuing ground-breaking global research initiatives, we educate and prepare future medical leaders and practitioners as part of our mission to ignite positive changes in the quality of health across the world.

The CAUSALab in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health is accepting applications for postdoctoral research fellows to work on research activities within the research lab. The CAUSALab launched in 2021 to articulate a growing research portfolio, create synergy with its strategic partners, and provide training on causal inference to the next generation of investigators.

This role is focused on pregnancy research:
To evaluate the effects of treatments in pregnancy we emulate target trials using observational large healthcare databases. Our team studies a range of interventions, from psychotropic agents to vaccines, in relation to multiple outcomes, from pregnancy losses to neurodevelopmental disorders in the infant. We apply state of the art methods to relevant clinical questions. This project includes two aims: a) to assess the benefits and risks of weight loss treatments for pregnancy preparedness;

and b) to assess whether babies exposed to fentanyl during pregnancy are at greater risk for birth defects (Fetal Fentanyl Syndrome).

Specifically, the postdoctoral research fellow will engage in the following:

• Perform data management of claims databases and prepare analytic files to implement causal inference study designs that follow the target trial framework
• Independently conduct the statistical analysis using SAS software or R
• Conduct literature reviews as necessary, prepare tables and figures, and participate in manuscript preparation and presentations
• Contribute to the development of detailed protocols including operational definitions, data dictionaries and ICD/CPT codes used for algorithms
• Will be responsible for making statistical analyses code publicly available on Git Hub ensuring the retention of appropriate documentation and preparation of metadata to foster replicability of results

Basic Qualifications:

Education Requirements:

A doctoral degree in epidemiology, biostatistics, or related field

Experience & Skills Requirements:
Two or more years of experience working with large complex healthcare databases, conducting statistical analyses with advanced methods and conducting causal inference student with observational data Export knowledge of R or SAS Writing manuscripts and presenting results Knowledgeable on Microsoft Office, End Note, or similar

Additional Qualifications:

Preferred Qualifications:

MD, PHhD, Master's in Data Science Analysis of US claims databases, specific experience with pregnancy studies Artificial intelligence methods for research Excellent writing and communication skills Organized and attentive to detail Self-motivated and can manage time independently Bring curiosity and eagerness to learn Ability to collaborate within CAUSALab and beyond the research group

Per university guidelines, postdoc appointments are considered to be on-campus, full-time positions. Per university payroll tax guidelines all applicants must reside in an acceptable payroll states or be willing to relocate to:
Massachusetts, New Hampshire, Rhode Island, Maine, Connecticut, Maryland, Vermont, New York, California, New Jersey, Virginia, Washington, Georgia, or Illinois.

Special Instructions:
Optional (but strongly recommended) writing A brief comment/critique (a few sentences suffice) of work conducted by CAUSALab researchers. For example, you can tell us how an analysis could be improved, propose alternative interpretations of the findings, point out potential errors, describe alternative areas of application, or discuss any ideas you'd like to share with us. Please upload this writing to…