Assistant Professor of Statistics with a focus in Data Science
Role details
Job location
Tech stack
Job description
The candidate selected for this position will be expected to teach and develop undergraduate and graduate courses in statistics and data science, support the development of the data science programs, keep an active research agenda, seek external funding, advise and supervise undergraduate and graduate students in statistics and data science, participate in service activities and provide consulting services to students and other faculty in the Department, College and University.
Requirements
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Ph.D. in statistics, biostatistics, data science, or a very closely related discipline (degree must be earned before 31 August 2026).
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Ability to conduct academic research and publish in high-quality peer-reviewed journals.
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Ability and interest in teaching graduate and/or undergraduate courses in statistics and data science.
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Ability to develop and maintain a collaborative and internally/externally funded research program in statistics, data science, and/or related fields.
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Strong interpersonal communication skills.
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Excellent command of written and spoken English.
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Ability to work with diverse groups/populations.
PREFERRED:
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Candidates with research interests in areas such as computational statistics, machine learning based statistical methods, large language models (LLMs), reinforcement learning (RL), analysis of large datasets, data-centric AI, and image analysis are especially encouraged to apply.
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Ability to develop curriculum at the undergraduate and graduate levels.
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Ability and/or experience advising and/or mentoring students.
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Interest in developing collaborative research and/or consulting to support the interdisciplinary nature of programs within the Department and the University as a whole.
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Commitment and/or interest in participating in service and outreach committees.
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Ability to improve recruitment and retention of undergraduate and graduate students.
Benefits & conditions
(#5) Other Document - Publication history document (limited to three pages), highlighting the type of contribution given to each manuscript (methodology, writing, coding, analyses, data acquisition) as one complete document.