New York, NY
Aug 21, 2023
The Department of Applied Statistics, Social Science, and Humanities (ASH) in conjunction with the Center for Practice and Research at the Intersection of Information, Society, and Methodology (PRIISM) seeks a computational social scientist for a tenure-track assistant professor position. The appointment begins September 1, 2024.
Applicants should have a keen interest in both developing/evaluating new statistical/computational methods and applying methods in collaborative work with faculty and graduate students in areas represented by the Applied Statistics program in ASH and the PRIISM Center, as well as with faculty and graduate students in other parts of the University whose interests intersect with these entities. Applicants would ideally have expertise in computational social science and statistics particularly for methods relevant to the social, behavioral, health, policy, or education sciences. This could include a methodological focus on Bayesian inference, causal inference, machine learning, missing data, multilevel models, networks or spatial statistics, or natural language processing, among other areas. The ideal candidate should have strong communication skills as a classroom teacher and as a presenter of research findings to others who are not as well versed in their field of research.
Responsibilities: In addition to being an outstanding researcher, the person appointed will contribute to the core teaching mission of the Applied Statistics program in ASH. Applied Statistics core faculty are members of a multi-disciplinary department of Applied Statistics, Social Science, and Humanities, and synergistic collaborations across these disciplines are encouraged. The position will involve teaching and advising students at both the undergraduate and graduate levels. The person appointed will supervise student research; develop an active program of research and publication; participate in faculty meetings and committees; and engage in other service appropriate to a university faculty member. The appointee will also be expected to secure external funding, as appropriate, to support their research.
In compliance with NYC's Pay Transparency Act, the annual base salary range for this position is $95,000 - $145,000. New York University considers factors such as (but not limited to) the scope and responsibilities of the position, the candidate's work experience, education/training, key skills, internal peer equity, as well as market and organizational considerations when extending an offer.
A doctoral degree in Statistics, Quantitative Social Science, or a related field (Computer Science, Economics, Political Science, Demography, Sociology, Psychology, Public Policy, etc) and the demonstrated ability to develop a nationally recognized research program. Priority will be given to those demonstrating the ability to connect statistical methods to social issues.
NYU's dynamic Global Network University includes NYU Abu Dhabi, NYU Shanghai, and international programs and academic centers around the world. NYU Steinhardt faculty may have the opportunity to engage in research and teaching at these global study and research sites.
Candidates should apply online by submitting the following: letter of interest in the position and university; statement of research describing past and planned research (3 pages max); statement of teaching detailing experiences and interests (2 pages max); curriculum vitae; statement about commitment to diversity, equity, and inclusion (2 pages max); three samples of scholarly writing; and three names of references (reference letters will only be solicited for some applicants before any campus visits occur.)
Review begins on September 25th, 2023, so for best consideration please apply before then.
For further information on the position please contact:
Associate Professor Daphna Harel, Chair of Search Committee
Email: daphna.harel@nyu.edu
This institution is using Interfolio's Faculty Search to conductthis search. Applicants to this position receive a free Dossieraccount and can send all application materials, includingconfidential letters of recommendation, free of charge.
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