Part Time Lecturer, MS Statistics and MS Applied Mathematics

Northeastern University•Boston, MA

About The Position

The Department of Mathematics seeks part-time lecturers to teach graduate-level project courses in the MS Statistics and MS Applied Mathematics programs. These courses are designed to help students apply mathematical, statistical, computational, and data-driven methods to real-world problems. The ideal candidate will have a strong academic background in mathematics, applied mathematics, statistics, data science, or a closely related field, together with professional or industry experience in applying quantitative methods. Project topics may include statistical modeling, machine learning, optimization, data analysis, mathematical modeling, AI-related applications, financial modeling, healthcare analytics, operations research, or other applied areas depending on program needs. The instructor will guide students through project development, problem formulation, data analysis, modeling, computation, interpretation, communication, and final presentations.

Requirements

PhD in Mathematics, Applied Mathematics, Statistics, Data Science, or a closely related field required.
Strong communication skills and ability to guide graduate students from diverse backgrounds required.
Ability to connect mathematical and statistical theory with practical applications required.

Nice To Haves

Industry, government, or research experience applying quantitative, statistical, computational, or data-driven methods strongly preferred.
Experience with project-based teaching, mentoring, consulting, or supervising applied projects preferred.
Experience with Python, R, MATLAB, or related tools strongly preferred.
Prior graduate-level teaching experience preferred.

Responsibilities

Teach graduate-level project-based courses in MS Statistics and MS Applied Mathematics.
Develop and guide applied projects connecting mathematics, statistics, computation, and real-world problems.
Mentor students through project scoping, modeling, data analysis, computation, interpretation, and presentation.
Support the use of tools such as Python, R, MATLAB, or other relevant software.
Provide timely feedback on project proposals, progress reports, presentations, and final reports.
Connect course content with current industry and research applications.
Maintain regular communication with students and hold office hours.
Coordinate with the program director and faculty lead as needed.