Research Data Associate Data

NYU Grossman School of Medicine is one of the nation's top-ranked medical schools. For 175 years, NYU Grossman School of Medicine has trained thousands of physicians and scientists who have helped to shape the course of medical history and enrich the lives of countless people. An integral part of NYU Langone Health, the Grossman School of Medicine at its core is committed to improving the human condition through medical education, scientific research, and direct patient care.

At NYU Langone Health, equity and inclusion are fundamental values. We strive to be a place where our exceptionally talented faculty, staff, and students of all identities can thrive. We embrace inclusion and individual skills, ideas, and knowledge.

The Research Data Associate will draw on diverse sets of data and programming skills, in order to accelerate and facilitate the research endeavors of the Environmental Pediatrics Division of the NYU School of Medicine. Specifically, this candidate will work closely with the Principal Investigator, Senior Data Manager and Research Scientist in order to collaborate on several projects that intend to measure the health and economic impacts of exposure to environmental chemicals in mothers, infants and adolescents.

Specifically, the candidate will be working on a large prospective cohort study of pregnant women and their children that aims to study the effects of prenatal and early‑life environmental exposures on children’s health and development. Women are recruited during pregnancy through their prenatal care visits and followed, along with their child, through the years of early childhood. Biological specimen (i.e., serum, urine, etc.)

are collected throughout pregnancy, at birth, and postnatally and stored for future analysis. This study will leverage data from questionnaires, medical records, and this biobank. The candidate will be responsible for processing, harmonizing, cleaning, and managing the research data from all sources, as well as conducting statistical analysis.

• Retrieve and assemble data from multiple sources, create derived datasets, and implement data specifications to transform study data into standard datasets.
• Perform and document data cleaning, post‑processing, and harmonization.
• Validate integrity and consistency of the data and resolve problems.
• Know and adhere to the accepted data standards, data management regulations and best practices, as well as security mechanisms required for PHI data.
• Ensure reproducibility by programming, maintaining, and documenting the research workflow.
• Generate datasets for data submission to external repositories under NIH and publishers’ data sharing policies.
• Conduct statistical analyses.

To qualify you must have an Associate’s degree plus one year related experience or equivalent combination of education and experience. Computer literate with good interpersonal, writing and verbal communication skills.

• Bachelor’s degree in epidemiology, quantitative science or related job experience. (Preferred: MPH)
• In‑depth understanding of data lifecycle: from data collection to data cleaning, processing, and preservation.
• Experience with programming languages (SAS and/or R).
• Knowledge of research processes.
• Experience with electronic data capture and database management systems.
• Experience in data transformation, report and query development.
• Familiarity with data standards.
• Excellent organizational, analytical, communication and interpersonal skills.
• Must be able to transition between assignments as needed and respond to work priorities as they arise.

Qualified candidates must be able to effectively communicate with all levels of the organization.

NYU Grossman School of Medicine provides its staff with far more than just a place to work. Rather, we are an institution you can be proud of, an institution where you’ll feel good about devoting your time and your talents. At NYU Langone Health, we are committed to supporting our workforce and their loved ones with a comprehensive benefits and wellness package.

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