DATA ANALYTICS - Part-time lecturer (non-tenure-track)

Job description

Tufts University School of Arts and Sciences


Part-time lecturer (non-tenure-track)

The Data Analytics program at Tufts University is seeking a part-time lecturer in Data Analytics to start September 1st, 2019.

The primary teaching interests of the successful candidate will be in data science and machine learning to include fundamentals of Python for data analysis: Python coding, statistical and computational methods, querying data sources, data engineering and management, and data visualization. The program is especially interested in candidates with experience in applications of data analysis and machine learning. The candidate will be expected to teach two back-to-back courses in the fall 2019 semester:

Introduction to Python for Data Analysis (DATA-201A)


Python and Machine Learning for Data Analysis (DATA-201B)


Qualifications. An M.S. or Ph.D. candidate in Computer Science, Math, Engineering or related discipline. Experience with data analysis and machine learning using Python is required.

All application materials must be submitted via Interfolio at . Please submit a cover letter, CV, sample course syllabus or outline, and two confidential letter of reference. Applicants should arrange to have the reference letters submitted directly by the authors to Interfolio.  Questions about the position may be directed to Jeff Zabel at [email protected] .

Review of applications will start immediately and continue until the position is filled.

Tufts University, founded in 1852, prioritizes quality teaching, highly competitive basic and applied research, and a commitment to active citizenship locally, regionally, and globally. Tufts University also prides itself on creating a diverse, equitable, and inclusive community. Current and prospective employees of the university are expected to have and continuously develop skill in, and disposition for, positively engaging with a diverse population of faculty, staff, and students.

Tufts University is an Equal Opportunity/Affirmative Action Employer. We are committed to increasing the diversity of our faculty and staff and fostering their success when hired. Members of underrepresented groups are welcome and strongly encouraged to apply. If you are an applicant with a disability who is unable to use our online tools to search and apply for jobs, please contact us by calling Johny Laine in the Office of Equal Opportunity (OEO) at 617-627-3298 or at [email protected]. Applicants can learn more about requesting reasonable accommodations at




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Job No:
Posted: 8/8/2019
Application Due: 10/7/2019
Work Type: