Senior Data Analyst

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 tomed.nyu.edu, and interact with us onLinkedIn,Glassdoor,Indeed,Facebook,TwitterandInstagram.

Position Summary:
We have an exciting opportunity to join our team as a Senior Data Analyst.

Dr. Sunita Desais lab is seeking a qualified Senior Data Analyst with strong data management and programming skills, particularly in the use of large scale CMS administrative claims data and other large health care sets and the use of quasi-experimental research methods. Our teams work applies quantitative causal inference methods to large health care databases including health care claims, electronic health records, and all-payer discharge data to answer health policy/economics research questions. Research projects address issues related to health care pricing, hospital markets, and the impacts of policies on health care spending and outcomes. The new team member will be responsible for data wrangling, analytic data set construction, and data analysis to support these projects and will ideally become involved and influential in decisions about the direction of the research and analysis. Through this work, analysts will gain skills and knowledge in the practical implementation of causal inference, data analysis, health care policies and payment systems, recent issues/trends in health care policy and the delivery system. This role will work closely with Dr. Desai and other lab members to work on several federally funded initiatives. The individual in this position will support multiple projects in day-to-day activities which include data collection, preliminary data analysis, data visualization, and results dissemination. The successful candidate will have a high degree of professional maturity, commitment to excellence, independence, flexibility, and initiative, and will possess the ability to work collaboratively with diverse individuals, teams, and populations both internally and externally.

Job Responsibilities:

Wrangle, clean, and maintain data from large administrative claims data sets and other sources in support of research protocols and projects.
Create secondary data sets from databases to support new and ongoing research.
Perform reviews to ensure accuracy and completeness of data.
Provide technical assistance and support to stakeholders to help them use data resources effectively and creatively.
Under supervision of research faculty, conduct statistical analyses and present them to the team and collaborators.
Support creation of tables and other data visualizations.
Participate in project meetings and workgroups to share knowledge and best practices
Produce publication ready tables and data visualizations
Think independently to identify data oddities, conceive of creative data visualizations, and suggest logical next steps
Contribute to team research project meetings
Performs other duties as assigned

Minimum Qualifications:
To qualify you must have a degree in economics, epidemiology, data science, biostatistics, health policy, or a related field
- an interest in health policy/economics research and quantitative methods
- experience with advanced regression methods and causal inference methods (differences-in-differences, regression discontinuity, etc.)
- comfort with handling large, relational data sets and working on servers
- highly proficient in R
- Ability to perform fuzzy merges of complex data sets
- Self-motivated and proactive with excellent organizational and communication skills

Preferred Qualifications:
Experience working with administrative claims data and exposure to quasi-experimental econometric methods
Action-oriented and eager to embrace new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm
Comfortable working in a fast-paced environment
Strong organizational, prioritization, and time management skills
Qualified candidates must be able to effectively communicate with all levels of the organization.

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 $67,771.14 - $93,483.52 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