How to Apply
In addition to applying to the position via UM Jobs, please follow the instructions below:
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: forms.gle/2YKAcheSjb1HgHGo6
Graduate Student Instructor (GSI) for Math 700: Directed Reading - Data Science in Quantitative Finance II
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), with 2 credits earned each term, and will culminate in students’ completion of a final project at the end of the second semester. This position will be the GSI for the second half of the course. 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 GSI will lead regular instruction on Fridays from 12:00 PM to 1:30 PM, while the primary instructor will lead instruction on select weekends (Fridays 12:00 PM- 1:30 PM and Saturday 10:00 AM – 2:00 PM)
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.)
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)
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
Please contact email@example.com with questions.
Decision Making Process
Applicants will be contacted by firstname.lastname@example.org
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, genetic information, marital status, familial status, parental status or pregnancy status, sex, gender identity or expression (whether actual or perceived), 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, medical conditions including those related to pregnancy, childbirth and breastfeeding, arrest record, 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.
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 their preferred assignment, they 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.
Standard Practice Guide 601.38, Required Disclosure of Felony Charges and/or Felony Convictions applies to all Graduate Student Assistants (GSAs). SPG 601.38 may be accessed online at spg.umich.edu/policy/601.38 , and its relation to your employment can be found in MOU 10 of your employment contract.
U-M EEO/AA Statement
The University of Michigan is an equal opportunity/affirmative action employer.