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How to Apply

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 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 # 176716
  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.

 

Job Summary

The School of Information has one (1) GSI position open, pending enrollment, to support five modules of its online master’s degree program in Fall 2019. This is an up .50 fraction position and offers a monthly stipend, tuition waiver, and health insurance. The expected work commitment is up to 20 hours per week.  There may be some flexibility in effort. This appointment runs from September 1 through December 31, 2019 (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.

Course Description

SIADS 505 Data Manipulation (Brooks- September)
Data Manipulation presents manipulation and cleaning techniques using the popular Python Pandas data science library. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

 
SIADS 502 Math Methods for Data Science (Ware- October)
Math Methods will review and establish math concepts that are foundational for a data scientist’s toolkit. Students will learn and apply concepts from linear algebra (such as matrices and vectors), basic optimization techniques (such as gradient descent) and statistics (such as bayes rule) in this course. 


SIADS 521 Visual Exploration of Data (Brooks- November)
Visual Exploration of Data teaches students how to look for (visually) aggregate patterns within data using the matplotlib library. Students will learn the challenges in visually exploring and representing analytic data with a focus on understanding how statistical measures can be applied. 


SIADS 631 Experiment Design and Analysis (Chen - November)
Experiment Design and Analysis presents experiment design for laboratory and field experiments. Students will discuss the logic of experimentation and the ways in which experimentation has been — and could be — used to investigate social and technological phenomena. Students will learn how to design experiments and analyze experimental data. 


SIADS 532 Data Mining I (Mei - December) 
Data Mining I introduces basic concepts and tasks of data mining. This course focuses on how to formally represent real world information as basic data types (itemsets, matrices, and sequences) that facilitate downstream analytics tasks. Students will learn how to characterize each type of data through pattern extraction and similarity measures.

Responsibilities*

Assist in the delivery of UMSI courses on Coursera, grading, holding online office hours, attend weekly staff meetings, managing autograders, answering student questions and communicating clearly with students, and facilitating small group online discussions and student conversations. 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.

Required Qualifications*

Graduate student in good standing. The applicant must have one or more of the following skill sets:
 

  1. Programming: proficiency in Python; pandas; numpy, scipy, regex, or beautiful soup libraries; and Jupyter Notebooks
  2. Mathematics: linear algebra, probability and statistics, strong algebra skills, preference for multivariable calculus. 
  3. Experiment Design: statistical principles of experiment design (power analysis, lab and field experiments), data analysis (regressions, differences-in-differences, instrumental variables).
  4. Data Mining -  Basic data types (itemsets, matrices, and sequences), pattern extraction, basic machine learning tasks,  large-scale data analytics

Desired Qualifications*

Experience or interest in teaching; strong communication and analytical skills; experience teaching programming and technology skills to beginning students.  

Background Screening

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.

Contact Information

UMSI Human Resources, umsi.human.resources@umich.edu 

Decision Making Process

Application due date: Monday, August 26, 2019.  Selection decisions will be completed as soon as possible, but no later than Friday, August 30, 2019.   Faculty instructors will be making the decision. 

Selection Process

Selection criteria will include: 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, 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 hr.umich.edu/oie/contact.html and for the University Ombuds at www.umich.edu/~ombuds/.

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.