Website Harvard University, Department of Government

The Government Department seeks applications for a full-time Senior Lecturer in quantitative methods. The appointment is expected to begin on July 1, 2023. The senior lecturer will be responsible for teaching three courses over the academic year, advising undergraduate senior theses, and helping to administer the undergraduate data science program.  This is a five-year position that is renewable contingent on performance, enrollments, and curricular needs.

Basic Qualifications: Candidates must have demonstrated that they are exceptional teachers and have taught within the tenured associate or full professor ranks at a university or peer undergraduate institution, with a commensurate scholarly record. They are required to have a Ph.D. in quantitative methods or related discipline.

Additional Qualifications: The position also requires evidence of effective teaching of introductory undergraduate methods courses.

Special Instructions: Please submit the following materials through the ARIeS portal ( Candidates are encouraged to apply by October 1; applications will be reviewed until the position is filled.

Cover letter
Curriculum Vitae
A teaching/advising statement that describes the candidate’s philosophy and practices related to undergraduate teaching and advising.
Research statement
Statement describing efforts to encourage diversity, inclusion, and belonging, including past, current, and anticipated future contributions in these areas.
Names and contact information of 3-5 references, who will be asked by a system-generated email to upload a letter of recommendation once the candidate’s application has been submitted. Three letters of recommendation are required, and the application is considered complete only when at least three letters have been received.

Harvard is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, sex, gender identity, sexual orientation, religion, creed, national origin, ancestry, age, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status.

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