Part Time Lecturer - Analytics (Boston)

Northeastern University•Boston, MA

1d•Onsite

About The Position

The College of Professional Studies at Northeastern University invites applications for a non-tenure-track, part-time faculty lecturer in Analytics on the Boston campus. The College offers undergraduate, graduate, and doctoral programs on campus and online, emphasizing lifelong experiential learning. The lecturer will teach analytics to global student populations, with instructional areas including probability and statistics, data exploration, data visualization, and predictive analytics. The Master of Professional Studies in Analytics program develops foundational analytics skills for working professionals, including data preparation, regression, forecasting, optimization, visualization, and data storytelling. Its experiential learning is closely linked to professional applications, preparing students for general entry- and mid-level analyst roles across professional domains. Applicants must submit a cover letter and vitae. The cover letter should explain how their expertise, knowledge, and skills prepare them to contribute to pedagogy and curriculum that foster belonging in the university community. Applications will be reviewed until the position is filled. Northeastern University will not sponsor an employment visa for this position; the successful applicant must maintain valid U.S. work authorization throughout the appointment without university visa sponsorship. The rate is $2,086.44 per credit.

Requirements

Master’s degree in an aligned discipline.
Experience in the analytics industry.
Demonstration of teaching, coaching, and/or training.
Ability to maintain valid U.S. work authorization throughout the appointment without Northeastern University visa sponsorship.

Nice To Haves

PhD in a sciences field, or similar.
A history of successful teaching at the graduate level, online or on ground.

Responsibilities

Teach analytics to global student populations.
Provide instruction in areas including probability and statistics, data exploration, data visualization, and predictive analytics.
Support development of foundational analytics skills, including data preparation, regression, forecasting, optimization, data visualization, and data storytelling.