Carnegie Mellon University — Assistant Professor, Statistics & Data Science

The Department of Statistics & Data Science at Carnegie Mellon University (www.stat.cmu.edu) invites applications for a tenure track position at the rank of Assistant Professor starting in Fall 2023. The Department seeks strong candidates in all areas of statistics and data science, as well as related interdisciplinary fields. Potential areas of interest include but are not limited to finance, social sciences, physical sciences and statistical computing. Excellent candidates with other research interests will also be considered and are highly encouraged to apply.

Applications received by December 1st, 2022 are guaranteed to receive full consideration.

Carnegie Mellon Statistics & Data Science is world-renowned for the significance of its contributions to statistical theory and practice and for its outstanding interdisciplinary applied research. Faculty in the Department are engaged in a wide range of theoretical, methodological and collaborative research. Current research by our faculty is helping to make fundamental advances in neuroscience, cosmology, networks, finance, genetics, public policy, high-dimensional inference, and theory and methods at the intersection of statistics and machine learning.

Collaboration, both within the Department and across the university, is a core value of the Department and a hallmark of the research work of our faculty. The Department boasts a very friendly and energetic working environment, and is dedicated to fostering, mentoring and supporting its junior faculty.

The Department is also widely recognized for advancing the teaching of Statistics and Data Science and for the excellence of our undergraduate program, with a large number of undergraduate majors and popular joint undergraduate programs in statistics and economics, and in statistics and machine learning. We also have two successful Master’s programs and a thriving Ph.D. program that attract exceptional students from around the world.

Diversity, equity, and inclusion, are core values of the Carnegie Mellon Department of Statistics & Data Science. We are committed to attracting candidates from historically under-represented groups knowing that diversity enriches the academic experience and provides a base for innovation and progress. We seek to meet the needs of dual-career couples and Carnegie Mellon is a member of the Higher Education Recruitment Consortium (HERC) that assists with dual-career searches. Carnegie Mellon University makes every effort to provide physical and programmatic access to individuals with disabilities. If you require an accommodation to participate in any part of the employment process, please contact Equal Opportunity Services by emailing employeeaccess@andrew.cmu.edu or calling 412-268-3930.

To learn more about the Equity, Diversity and Inclusion Plan of CMU Dietrich College, please visit: https://www.cmu.edu/dietrich/about/dei/index.html.

Qualifications
PhD (or equivalent international degree) or enrolled in PhD (or equivalent international degree generating program) at the time of application. Candidates will be expected to complete their PhD or equivalent degree no later than the start date of the appointment (August, 2023).

Application Instructions
To apply for the position, interested candidates must include a cover letter, a complete curriculum vitae, a research statement, a statement of teaching interests, and the names and contact information (email addresses) for writers of at least 3 letters of recommendation.

Candidates can include their contributions to diversity, equity and inclusion (DEI), through their teaching and mentoring, in their statement of teaching. Other contributions to DEI through for instance, their research or service can be included in the candidates’ cover letter, and research statement. These contributions can include both your activities to date and your future plans and goals.

Review of applications will begin December 1, 2022 and the position will remain open until filled.

Apply Now:
https://apply.interfolio.com/115882

Related: