How to Apply
The School of Information has up to 1 GSI position at .50 fraction, pending enrollment, open for applications.
A .50 fraction GSI position offers a monthly stipend, tuition waiver, and health insurance. The expected work commitment is roughly 20 hours per week. This appointment runs from August 29 to December 31, 2022 (from an employment/health benefit perspective). There could be prep time before the employment start date and all work for this course will be completed by the end of December (or slightly earlier). All of the work hour details will be spelled out in the fraction calculation form for the person hired for this position.
Please indicate your interest by submitting a cover letter and resume electronically using the careers.umich.edu website. Below are some instructions to help you through this application process.
1. Go to http://www.umjobs.org
2. Click on "Login" (upper right corner). Use your umich uniqname and password.
3. Click on "U-M Graduate Student on the Ann Arbor campus" identifying yourself as a UM Graduate Student (fourth option)
4. Click in the "Search for Jobs" box at the top of the page
5. Enter the Job Opening ID # 220573
6. You are now in the standard application. Answer all questions and proceed through the application process as prompted. Upload your application as one document (preferably a Word or PDF document) including your cover letter with information on availability, your resume, and any teaching evaluations*
7. Click "Submit" when you are finished.
*Having trouble uploading your document?
The most common cause of upload and display issues can be attributed to an unsupported operating system or internet browser. Internet Explorer is the browser of choice when using the site, however, if one browser doesn't seem to be working properly, switch to a different browser and/or clear your cache and cookies.
Double check your document type. The system accepts resumes/cover letters created in a .DOC, .DOCX, .PDF, .TXT .HTML or .RTF. Uploading your resume/cover letter as a Microsoft Word document is the recommended format. File names are limited to 35 characters or less and cannot contain punctuation marks or special characters.
Milestone II --- This course represents a key assessment point for fundamental data analytics skills and practices applied to realistic data science problems. Students will complete project-based work with guidance from the instructor, as well as a comprehensive exam.
More information about these courses can be found on U-M’s Course Catalog via Wolverine Access.
Assist in the delivery of UMSI courses on Coursera: grading, holding online office hours; holding meetings with, and providing technical assistance to, student project teams; attending weekly staff meetings; managing autograders; answering student questions and communicating clearly with students as needed for projects, and facilitating small group online discussions and student conversations. This is a project-based course, so the emphasis is less on weekly grading and more on supporting student teams across a wide variety of interesting data science projects. Demonstrate respect for students as individuals and foster a respectful atmosphere in the online learning environment. This position will also be expected to work collaboratively with lead instructors and other instructional team members.
Graduate student in good standing;
Must meet eligibility criteria as defined in the GEO contract;
Applicants must be lawfully able to be employed in the United States, sponsorship to obtain such status is not available at this time;
Previous success as an SIADS 694 and 695 student or equivalent combination of course work and experience;
The applicant must have at least one, and ideally all, of the following skill sets:
1. Scientific Programming and Visualization: proficiency in Python; pandas; numpy, scipy; matplotlib and scikit-learn, ideally via Jupyter Notebooks.
2. Mathematics: strong linear algebra, probability and statistics skills.
3. Experiment Design: statistical principles of experiment design (power analysis, lab and field experiments), data analysis (regressions, differences-in-differences, instrumental variables).
4. Machine Learning/Data Mining - basic data types and formats (itemsets, matrices, time-series and sequences), as well as using those data types in pattern extraction, supervised and unsupervised machine learning tasks and/or large-scale data analytics. Causal inference and/or text analytics/natural language processing are a plus, but not required.
Experience or interest in teaching; strong communication and analytical skills; experience teaching programming and technology skills to beginning students, especially machine learning and data mining, and project management skills.
This position works remotely; however, candidates must be able to conduct work in the United States. Employment outside of the United States or from a U.S. Territory is not allowable at this time.
The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third party administrator to conduct background checks. Background checks are performed in compliance with the Fair Credit Reporting Act.
Please contact firstname.lastname@example.org with any questions.
Decision Making Process
Application due date: Tuesday, August 9, 2022. Selection decisions should be completed by Friday, August 19, 2022. Professor Eric Gilbert will be making the decision.
Relevant preparation for teaching the course material; extent of prior instructional/work experience relevant to the course and relevant to the GSI requirements for this course; demonstration of explanatory skills; position’s relevance to graduate training; previous student evaluations if applicable; availability for course time requirements.
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 https://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.
U-M COVID-19 Vaccination Policy
COVID-19 vaccinations, including boosters when eligible, are required for all University of Michigan students, faculty and staff across all campuses, including Michigan Medicine. This includes those working remotely. More information on this new policy is available on the Campus Blueprint website or the UM-Dearborn and UM-Flint websites.