NYU Grossman Long Island School of Medicine

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.. 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.. 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.. To qualify you must have a Masters or doctoral degree in epidemiology, biostatistics/statistics, data science, economics, public health, or a related quantitative field. Experience studying public health policy and/or substance use and mental health policy and programming

research scientist

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 med.nyu.edu , 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. Job Responsibilities <ul><li> Gather, analyze, and interpret a wide variety of research data </li><li> Compile, clean, merge, harmonize, and maintain databases from multiple administrative sources </li><li> Carry out complicated, in-depth descriptive and inferential statistical analyses using primary and secondary data, including analyses for longitudinal studies and multi-level data structures </li><li> Learn predictive models using a range of machine learning methods </li><li> Map data elements at multiple levels of geography in a GIS framework </li><li> Collect and compile data from novel sources such as public records and via webscraping </li><li> Contribute to the development of analytic plans in consultation with investigators; recommend solutions to analytical challenges </li><li> Prepare results of analyses for review; participate in discussion and interpretation of findings </li><li> Participate in manuscript development, revision, and submission </li><li> Create text, tables, figures, and charts for presentations and publications </li><li> Conduct literature reviews </li><li> Maintain regular communication with project team members, including external stakeholders. </li><li> Assist with the development of grant applications for future funding </li><li> Prepare and submit Institutional Review Board applications </li><li> Prepare and submit proposals and amendments for data use agreements </li><li> Conduct project management activities as needed </li></ul>Minimum Qualifications To qualify you must have a Masters or doctoral degree in epidemiology, biostatistics/statistics, data science, economics, public health, or a related quantitative field Demonstrated experience applying epidemiological and econometric methods to administrative healthcare and survey data Demonstrated experience applying predictive modeling and machine learning methods to healthcare data Demonstrated experience creating, managing and analyzing large databases of linked health data across multiple levels of geography Ability to gather, organize, analyze, and report findings from analyses Proficiency with data visualization Programming expertise in analytic software, such as R or Python Detail-oriented and able to prioritize and alter priorities as circumstances require Ability to work both independently and collaboratively Willingness and ability to learn new methodological techniques Strong quantitative, organizational, writing, formatting, and social skills Knowledge and application of project management principles and concepts Proven ability to design complex data management and analysis projects, working with professionals with a variety of skills and backgrounds Preferred Qualifications Experience studying public health policy and/or substance use and mental health policy and programming Experience innovating and streamlining ways to structure, analyze, and present data in tables and figures Ability to carry out in-depth statistical analyses using primary and secondary data, including analyses for longitudinal studies and multi-level data structures Training and experience with modern epidemiologic and/or econometric approaches to causal inference including the potential outcomes framework, g-computation, inverse probability weighting, matching, differences-in-differences, synthetic control, etc. Training and experience with machine learning approaches, including spatiotemporal, tree-based, and deep learning methods Experience with webscraping Living in or willing to commute to New York City 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. NYU Grossman School of Medicine is an equal opportunity and affirmative action employer committed to diversity and inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration without regard to race, color, gender, gender identity or expression, sex, sexual orientation, transgender status, gender dysphoria, national origin, age, religion, disability, military and veteran status, marital or parental status, citizenship status, genetic information or any other factor which cannot lawfully be used as a basis for an employment decision. We require applications to be completed online. If you wish to view NYU Grossman School of Medicine's EEO policies, please click here . Please click here to view the Federal "EEO is the law" poster or visit https://www.dol.gov/ofccp/regs/compliance/posters/ofccpost.htm for more information. NYU Langone Health provides a salary range to comply with the New York state Law on Salary Transparency in Job Advertisements. The salary range for the role is $58,500.00 - $60,949.05 Annually. Actual salaries depend on a variety of factors, including experience, specialty, education, and hospital need. The salary range or contractual rate listed does not include bonuses/incentive, differential pay or other forms of compensation or benefits. To view the Pay Transparency Notice, please click here

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