Assistant Professor in Data Science

Position Summary

The Department of Mathematics and Statistics at the University of New Mexico invites applications for a full‑time, tenure‑track Assistant Professor position in Data Science, with a start date of August 2026. Applicants must hold a Ph.D. in Mathematics, Statistics, Computer Science, or a closely related field by the appointment start date.

The successful candidate will develop a strong independent research program in one or more areas central to modern Data Science, such as Optimization, Machine Learning, Statistical Learning, Applied Probability, Graph Theory and Network Science, or Computational Statistics. The position encourages cross‑disciplinary collaboration across UNM and with national laboratories such as Sandia National Laboratories.

Core teaching responsibilities will include developing and delivering the following courses in the Data Science concentration:

• Introduction to Data Science
• Probability and Statistics for Data Science
• Optimization and Algorithms
• Data Mining and Machine Learning
• Capstone in Data Science

The new faculty member will also support course needs in Applied or Pure Mathematics and Statistics, strengthening the department’s capacity to serve diverse student populations and client departments across the university.

This hire is a cornerstone of the newly approved Data Science Concentration, which integrates departmental strengths across Statistics, Pure Mathematics, and Applied Mathematics. The position will support this high‑impact, interdisciplinary initiative and position our department as a campus leader in modern data‑driven education and research.

• Cover letter – applicants are encouraged to include 2‑3 sentences on how their teaching, research, or service may contribute to UNM’s mission to serve local and global communities.
• Curriculum vitae
• Research statement
• Teaching statement
• Minimum of four letters of recommendation, including at least one addressing teaching experience – letters may be sent to Amy Hathaway at ahathawa or by mail to:
  Search Committee – Data Science Position
  Department of Mathematics and Statistics
  MSC
  01 1115, 1 University of New Mexico
  Albuquerque, NM 87131

For best consideration, completed applications should be received by October 15, 2025. Applications with fewer than four letters of recommendation will not be considered. The position will remain open until filled.

Applicants who are appointed to a UNM faculty position are required to provide official certification of successful completion of all degree requirements prior to their initial employment with UNM.

• Dr. Mohammad Motamed, Search Committee Chair – motamed
• Dr. Monika Nitsche, Department Chair – nitsche
• Technical or submission issues – Amy Hathaway, ahathawa

Minimum Qualifications:

• Ph.D. in Mathematics, Statistics, Computer Science, or a closely related field by the appointment start date
• Research experience in one or more areas central to modern Data Science (e.g., Optimization, Machine Learning, Statistical Learning, Applied Probability, Graph Theory and Network Science, or Computational Statistics)
• Teaching experience at the university level

Preferred Qualifications:

• Proven record of impactful research in one or more of the above areas
• Ability to contribute to the curriculum of the Data Science concentration and broader departmental needs
• Demonstrated excellence in teaching, including mentoring students at all levels
• Engagement in cross‑disciplinary collaboration with UNM departments or national laboratories such as Sandia National Laboratories
• Commitment to cultivating an understanding of the rich and varied cultures of New Mexico and to the university’s mission to serve local and global communities
• Postdoctoral experience

UNM is an Equal Opportunity/Affirmative Action Employer. All qualified applicants are encouraged to apply.

The University of New Mexico is committed to hiring and retaining a diverse workforce. We are an Equal Opportunity Employer, making decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, veteran status, disability, or any other protected class.