Research Scientist - New York, United States - NYU Langone Health

Mark Lane

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Mark Lane

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Description

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, diversity, 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 diversity, inclusion, and individual skills, ideas, and knowledge. _For more information, go to , and interact with us on _LinkedIn_, _Glassdoor_, _Indeed_,_ _Facebook_, _Twitter_ and _Instagram_._


Position Summary:

We have an exciting opportunity to join our team as a Research Scientist.


The Center for Opioid Epidemiology and Policy (COEP) in the Department of Population Health (DPH) at the NYU Grossman School of Medicine is seeking a Research Scientist to support multiple research projects.

COEP conducts high-impact research to inform evidence-based policies and programs to prevent overdose and other drug-related harms. Particular focus is on the development of strategies to reduce racial/ethnic and economic inequalities in these outcomes.

The Research Scientist will work with COEP faculty member Dr. Bennett Allen to support several streams of work.

Projects may include, but are not limited to:
the evaluation of the impact of novel harm reduction interventions, including overdose prevention centers, on overdose and other drug-related health outcomes; the development of individual
- and area-level predictive models for overdose and other drug-related health conditions; the evaluation of programs and policies at the intersection of substance use, mental health, and the criminal legal system; the evaluation of public health and public safety partnership interventions. These projects will be conducted using epidemiological, econometric, and machine learning methods. The integration of epidemiological and machine learning methods is central to COEPs work. The majority of work will be conducted in partnership with community-based and government organizations.


The Research Scientist will work with internal and external stakeholders to compile, integrate, maintain, and analyze complex administrative and survey datasets across the areas described above.

The Research Scientist will coordinate data acquisition and management, lead data analyses, manage research administration, including IRB proposals and data use agreements, and prepare reports, manuscripts, and proposals related to COEP studies.

Opportunities exist for the Research Scientist to engage in publication and public presentation of findings as desired. Crucially, the Research Scientist will have the skillset and latitude to independently conduct complex analyses under Dr. Allens supervision.

The Research Scientist will be integrated into the vibrant and diverse research community at COEP and within DPH.

As a member of COEP, the Research Scientist will have access to a community of Data Analysts and Research Scientists across COEP and DPH, monthly open analyst meetings, career development workshops and sessions, journal clubs in epidemiology, biostatistics, and health equity, seminars, and COEP-specific meetings and events.

Opportunities exist for collaboration across NYU Langone Health, including with clinical departments. Applicants from backgrounds under-represented in epidemiology and the medical sciences are welcomed and encouraged to apply.


Responsibilities:


  • Gather, analyze, and interpret a wide variety of research data
  • Compile, clean, merge, harmonize, and maintain databases from multiple administrative sources
  • Carry out complicated, indepth descriptive and inferential statistical analyses using primary and secondary data, including analyses for longitudinal studies and multilevel data structures
  • Learn predictive models using a range of machine learning methods
  • Map data elements at multiple levels of geography in a GIS framework
  • Collect and compile data from novel sources such as public records and via webscraping
  • Contribute to the development of analytic plans in consultation with investigators; recommend solutions to analytical challenges
  • Prepare results of analyses for review; participate in discussion and interpretation of findings
  • Participate in manuscript development, revision, and submission
  • Create text, tables, figures, and charts for presentations and publications
  • Conduct literature reviews
  • Maintain regular communication with project team members, including external stakeholders.
  • Prepare and submit proposals and amendments for data use agreements
  • Conduct project management activities as needed
**Minimum

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