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Important: You can only submit one document with your application. The name of the file you upload MUST contain your full name. After submitting an application, applicants should fill out the Application Supplement form here in lieu of a cover letter:

Mission Statement

The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.  The University of Michigan is committed to foster learning, creativity and productivity, and to support the vigorous exchange of ideas and information, not only in the classroom but in the workplace by: 

  • Creating a work environment in which people treat each other with respect and dignity, regardless of roles, responsibilities or differences. 
  • Providing support, direction and resources enabling us to accomplish the responsibilities of our jobs and to reach the goals that are set for professional and personal growth.

Course Description

Winter 2020

Graduate Student Instructor (GSI) for Math 652: Topics in Applied Mathematics - Data Science in Quantitative Finance I

This is a two-credit special topics course intended for students in the Master’s Program in Quantitative Finance and Risk Management (Quant Program). The aim of the course is to prepare students in the Quant Program to meet the needs of finance industry employers by providing the students with a theoretical understanding of and practical experience in applying data science concepts as they pertain to financial mathematics. The course will focus on mathematical foundations, practical programming exercises, domain expertise, and technical communication and will be divided into the following content areas:

I. Classical Statistical Learning (Classification, Regression, Support Vector Machine, Nearest Neighbors)

II. Ensemble Learning, Dimensionality Reduction

III. Neural Networks, Deep Networks

IV. Model Interpretability, Feature Importance, Feature Reduction

Course content will be taught across two terms (Winter 2020 and Fall 2020) and will culminate in students’ completion of a final project at the end of the second semester.

Being a GSI for this course gives you the unique opportunity to work under the primary instruction of a current industry professional who specializes in machine learning in quantitative finance. The instructor will be based remotely, with on-campus instruction tentatively taking place on the following Fridays and Saturdays in Winter 2020: January 31 and February 1, February 21 and 22, and March 20 and 21. The GSI will lead regular instruction on Fridays in the late afternoon or evening (exact times TBD) throughout the semester.


This is a 50% GSI position. The anticipated workload is 16.5 to 20 hours per week. The GSI will work closely with the instructor on all facets of the course in order to help meet student needs. Duties include but are not limited to the following: facilitate weekly class meetings, collaborate with instructor to support the creation of course materials, prepare and deliver some lessons, hold weekly office hours, assist with grading of homework and projects, communicate with instructor regularly, administrative tasks (copying, etc.)


Required Qualifications*

  • Graduate student in good standing in the University
  • Previous experience as a GSI, with favorable student evaluations
  • Graduate level coursework in machine and/or statistical learning
  • Graduate level coursework in computer science and programming (Python)

Desired Qualifications*

  • PhD student in Computer Science, Statistics, or Mathematics
  • Research and/or significant project experience in machine learning and/or statistical learning
  • Significant project experience in Python
  • Coursework or demonstrated interest in financial engineering or financial mathematics

Contact Information

Please contact with questions.

Decision Making Process

Priority will be given to applications submitted by 4 pm on October 16, 2019. Initial in-person interviews will take place on Friday, October 18, 2019, with the possibility of further interviews later on.

Selection Process

Selection Criteria will include:

  • Relevant academic preparation for teaching the course material
  • Extent of prior instructional experience
  • Relevance to graduate training

GEO Contract Information

The University will not discriminate against any applicant for employment because of race, creed, color, religion, national origin, ancestry, marital status, familial status, parental status or pregnancy status, sex, gender identity or expression, sexual orientation, age, height, weight, disability, citizenship status, veteran status, HIV antibody status, political belief, membership in any social or political organization, participation in a grievance or complaint whether formal or informal, or any other factor where the item in question will not interfere with job performance and where the employee is otherwise qualified. The University of Michigan agrees to abide by the protections afforded employees with disabilities as outlined in the rules and regulations which implement Section 504 of the Rehabilitation Act of 1973 and the Americans with Disabilities Act.

Information for the Office of Institutional Equity may be found at and for the University Ombuds at

Unsuccessful applications will be retained for consideration in the event that there are last minute openings for available positions. In the event that an Employee does not receive his or her preferred assignment, he or she can request a written explanation or an in-person interview with the hiring agents(s) to be scheduled at a mutually agreed upon time.

This position, as posted, is subject to a collective bargaining agreement between the Regents of the University of Michigan and the Graduate Employees' Organization, American Federation of Teachers, AFL-CIO 3550.

U-M EEO/AA Statement

The University of Michigan is an equal opportunity/affirmative action employer.