The Department of Statistics, Actuarial and Data Sciences (STAD) at Central Michigan University invites applications for a full-time fixed-term faculty position in Actuarial Science for the 2021-2022 academic year starting August 23, 2021. PhD or ABD with expertise in Actuarial Science Statistics or related areas and has passed at least two Actuarial Exams is preferred. Courses to be taught may include levels ranging from introductory to senior level courses in actuarial science or statistics. The teaching load is 12 credit hours per semester. Applicants without a PhD or ABD must be an Associate/Fellow of the Actuarial Society.
To apply for the position, please visit:
to submit an online application.
The application documents should include a cover letter, a recent CV and contact information for three references. The review of applications will begin on March 1st, 2021 and continue until the position is filled. Questions about the position should be directed to the department chair, Dr. Carl Lee at
The STAD Department was officially launched in fall 2019. The department offers PhD in Statistics and Analytics, MS in Applied Statistics and Analytics, graduate certificate programs in Data Mining and in Actuarial Science, BS in Actuarial Science, BS in Statistics, and is developing a BS in Data Science and a minor in Actuarial and Risk Analytics to be launched in fall 2022. The STAD department has eight tenured faculty with active research in distributions, modeling, Bayesian analysis, actuarial and risk analysis, and data analytics. The Actuarial Science program is designed to prepare students for four actuarial exams. The Actuarial Science program is ranked number 22 in the nation by College Choice Net. More information about the department can be found on this website. "CMU, an AA/EO institution, strongly and actively strives to increase diversity and provide equal opportunity within its community. CMU does not discriminate against persons based on age, color, disability, ethnicity, familial status, gender, gender expression, gender identity, genetic information, height, marital status, national origin, political persuasion, pregnancy, childbirth or related medical conditions, race, religion, sex, sex-based stereotypes, sexual orientation, transgender status, veteran status, or weight (see