Tenure-Track/Tenured Faculty Positions in Statistics and Data Science (2026-2027)

University of Texas at Austin; Austin (TX); On-site; USD 120,000 - 190,000; Full time

Job summary

The Department of Statistics and Data Science (SDS) at The University of Texas at Austin invites applications for tenured or tenure-track faculty positions, at any rank, to begin in August 2027. We seek exceptional scholars whose work advances our ability to learn from data in diverse applications or by deepening statistical inference and learning.

We are particularly interested in four directions: applied statistics and AI for science; causal inference and experimental design; statistical

Qualifications

• PhD in statistics, biostatistics, computer science, machine learning, or a closely related discipline by August 2027.
• Candidates for tenure-track positions evaluated on potential for an impactful research program, teaching, mentoring and service.

Responsibilities

• Develop and lead an independent research program with external funding.
• Teach and mentor students at graduate and undergraduate levels.
• Provide service to the department, university, and broader professional community.

Education

Doctoral degree

Job description

Tenure-Track/Tenured Faculty Positions in Statistics and DataScience (2026-2027)

College/School/Unit: College of Natural Sciences
Department: Department of Statistics and Data Science
Posted: Sep 14, 2026
Apply By: Nov 23, 2026

Description

The Department of Statistics and Data Science (SDS) at TheUniversity of Texas at Austin invites applications for tenured ortenure-track faculty positions, at any rank, to begin in August2027. We seek exceptional scholars whose work advances our abilityto learn from data, whether by directly addressing importantempirical questions in a chosen area of application, or bydeepening the foundations of statistical inference and learning.

We are particularly interested in four broad directions. First, weseek candidates in applied statistics, scientific machine learning,or AI for science: methodologists embedded deeply enough in anotherfield that they help shape its scientific questions and build newdata-analysis methods in response. The field may be any area of thenatural, biomedical, computational, engineering, or socialsciences. For such candidates, we regard publication in the leadingvenues of their chosen field as central evidence of impact, onequal footing with publication in statistics and machine-learningvenues. No matter the area, the candidate's record should exhibitmethodological advances that statisticians and machine-learningresearchers would recognize as such, even if they first appeared ina domain journal.

Second, we welcome research in causal inference, experimentaldesign, and related areas concerned with learning frominterventions and designing informative studies. This includesfoundational and applied work on how interventions are identifiedand evaluated, how evidence generalizes across settings, and howexperiments and other data-collection strategies can be designed toanswer important questions.

Third, we seek scholars working on statistical theory, includingthe foundations of machine learning and AI. We are interested infundamental questions about inference, uncertainty, information,learning, robustness, computation, and decision-making. We valuetheoretical and computational work that provides new understandingof (or new broad capabilities for) statistics, machine learning,and AI, whether or not it is tied to an immediateapplication.

Fourth, we welcome work that creates broadly useful newcomputational tools for data-analytic practice, such as statisticalcomputing environments and languages, probabilistic programmingsystems, scalable inference software, and interactive orAI-assisted tools for data analysis. For such candidates,widespread adoption of their tools by researchers and practitionersis evidence of impact on equal footing with publication.

SDS is one of three founding departments of UT's new School ofComputing, alongside Computer Science and Information. We welcomecandidates whose work creates opportunities for collaboration withcolleagues in those departments or elsewhere at The University ofTexas at Austin, including through joint appointments.

Our department is internationally recognized for its research instatistical methodology and theory, machine learning, appliedstatistics, Bayesian inference, and biostatistics, and its facultyare committed to excellent teaching in statistics and data sciencefor students from across the School, the University, and in SDS'sown degree programs. The department currently has 26tenured/tenure-track faculty, including 9 joint faculty withprimary appointments in other departments. UT Austin is one of themost intellectually vibrant universities in the country, withabundant opportunities for interdisciplinary research within theCollege of Natural Sciences and School of Computing, and across theDell Medical School, the Oden Institute for ComputationalEngineering and Sciences, the Population Research Center, theMachine Learning Laboratory, the Center for Generative AI, and manyother research centers across the campus. A partnership with theTexas Advanced Computing Center (TACC) provides access toworld-class computing resources. The department and university arecommitted to supporting the professional development of all membersof the faculty. The teaching load for tenured/tenure-track facultyin SDS is two courses per year.

Austin, the capital of Texas, is a center for high-technologyindustry, including companies such as 3M, Amazon, AMD, Apple,Applied Materials, AT&T, Dell, Google, IBM, NationalInstruments, and Samsung. Much of Austin's lifestyle is driven byoutdoor activities, media, and music.

More information about the department is here.

Qualifications

Candidates should have a doctoral degree in statistics,biostatistics, computer science, machine learning, appliedmathematics, or a closely related discipline by August 2027.

Candidates for a tenure-track Assistant Professor position areevaluated based on their potential for developing an impactfulresearch program and for becoming excellent in teaching, mentoring, and service.

Candidates for a tenured Associate or Full Professor position areexpected to exhibit a strong independent program of externallyfunded research along with established records of excellence inteaching, mentoring, and service.

Equal Employment Opportunity Statement

The University of Texas at Austin, as an equalopportunity/affirmative action employer, complies with allapplicable federal and state laws regarding nondiscrimination andaffirmative action. The University is committed to a policy ofequal opportunity for all persons and does not discriminate on thebasis of race, color, national origin, age, marital status, sex,sexual orientation, gender identity, gender expression, disability,religion, or veteran status in employment, educational programs andactivities, and admissions.