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Lab Instructors (Course Assistants) for MSSP 6300: Quantitative Reasoning
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Position Number: 5571645
Location: Philadelphia, PA
Position Type: Science - Statistics

Lab Instructors (Course Assistants) for MSSP 6300: Quantitative Reasoning

University Overview

The University of Pennsylvania, the largest private employer in Philadelphia, is a world-renowned leader in education, research, and innovation. This historic, Ivy League school consistently ranks among the top 10 universities in the annual U.S. News & World Report survey. Penn has 12 highly-regarded schools that provide opportunities for undergraduate, graduate and continuing education, all influenced by Penn's distinctive interdisciplinary approach to scholarship and learning. As an employer Penn has been ranked nationally on many occasions with the most recent award from Forbes who named Penn one of America's Best Large Employers in 2023.

Penn offers a unique working environment within the city of Philadelphia. The University is situated on a beautiful urban campus, with easy access to a range of educational, cultural, and recreational activities. With its historical significance and landmarks, lively cultural offerings, and wide variety of atmospheres, Philadelphia is the perfect place to call home for work and play.

Posted Job Title
Lab Instructors (Course Assistants) for MSSP 6300: Quantitative Reasoning

Job Profile Title
Temporary Employee - Non-Exempt

Job Description Summary
Temporary Employee - Non-Exempt

Job Description

Course description: This course provides an introduction to statistical inference. We will learn the fundamental tools of data science and apply them to a wide range of social science and policy-oriented questions. The objective of the course is to develop two broad skill sets: (1) an understanding of the conceptual foundations for why we might manage or analyze data in one way versus another, and (2) learning the computing and programming tools (using R) to manage, visualize, and analyze data. The topics covered in the course include descriptive statistics, measure of association for categorical and continuous variables, introduction to t-tests, ANOVA and linear regression, research design (e.g., sampling, measurement, and causal inference), and the language of data analysis. Students will learn how to apply statistical tools to data sources, to design research studies, to test hypotheses, and to interpret the results of quantitative studies. The lecture focuses on the conceptual foundations of statistical inference; R programming instruction is covered in the weekly lab sections.

Requirements:

Strong proficiency in R and RStudio, especially in relation to troubleshooting coding errors. Strong background in working with data: mastery of intermediate statistics & probability theory. Attentiveness to details, committed, willing to go the extra mile for students. Ideally, some research experience. Lab instructors independently run a discussion section. Enthusiasm for data!-and ability to relay/discuss data.

The Lab Instructor must have strong mastery of the course content as demonstrated by relevant degrees, classes taken, and/or professional experience.

Lab Instructor duties include the following:

Manage the Canvas site Attend and assist students during lectures (this includes troubleshooting R code, and answering questions & aiding discussion during in-class exercises) Design lab sessions (based on lab materials, provided by head Lab Assistant) Instruct weekly lab sessions Help develop problem sets; contribute to the design of some assignments Grade problem sets, lab assignments, and class participation Answer students' questions about course logistics, course content, and grading. The questions will be answered on email and/or Ed Discussion, as determined by the instructor. Difficult questions are escalated to the instructor. Participate in brief, weekly LA meetings with professor(s) to discuss course.

Lab Instructors (Course Assistants) can expect to work approximately 7-10 hours/week (max 100hrs total) and are paid $50/hour up to $5,000 over the course of the semester.

To apply, please send the following to msspprogram@sp2.upenn.edu:

Your CV/resume
A list of relevant coursework taken as a student or led as a TA
A brief email/cover letter detailing your relevant experience
A list of your availability for lecture and lab sections
Submit a 5min video of you teaching how to: 1) import a simple two-variable data set into R; and 2) generate a scatterplot with a registration line. Upload the video via Zoom (instructions below).

To submit your video:

Record your video on Zoom. Start a new meeting + click on record. Record to the cloud.
Once your video is complete, Zoom will send you an email.
To share the video with MSSP, please see Zoom instructions on how to "How to share, download, and delete cloud recordings" at: https://support.zoom.com/hc/en/article?id=zm_kb&sysparm_article=KB0067567
(you will have to scroll down the page a bit for the relevant section).

For questions about the course, please email alicezxu@upenn.edu.

Job Location - City, State
Philadelphia, Pennsylvania

Department / School
School of Social Policy and Practice

Pay Range
$7.25 - $50.00 Hourly Rate
Salary offers are made based on the candidate's qualifications, experience, skills, and education as they directly relate to the requirements of the position, as well as internal and market factors and grade profile.

Affirmative Action Statement
Penn adheres to a policy that prohibits discrimination on the basis of race, color, sex, sexual orientation, gender identity, religion, creed, national or ethnic origin, citizenship status, age, disability, veteran status, or any other legally protected class.

Special Requirements
Background check may be required after a conditional job offer is made. Consideration of the background check will be tailored to the requirements of the job.

To apply, visit https://wd1.myworkdaysite.com/en-US/recruiting/upenn/careers-at-penn/job/Sch-PubPol-Caster-Building/Lab-Instructors--Course-Assistants--for-MSSP-6300--Quantitative-Reasoning_JR00094105-1