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# Learning Analytics Data Scientist

- **Company:** Pennsylvania State University
- **Location:** State College, PA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $91,488.0 - $137,280.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Cluster Analysis, Continuous Delivery, Continuous Integration, Learning Management Systems, Data Cleansing, Data Structures, Data Visualization, Relational Databases, Software Debugging, Github, Information Sciences, Python (Programming Language), Machine Learning, NumPy, Student Information Systems, Freeform SQL, Feature Engineering, Fastapi, Pandas, Matplotlib, Information Technology, Data Analytics, Plotly, Restful APIs, Code Restructuring, Software Version Control
- **Published:** October 23, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/88681147/1

## About the Role

Penn State IT invites a Learning Analytics Data Scientist to join the award-winning Data Empowered Learning team. In this role, you will assist in the creation of the models and insights that power cutting-edge tools like Course Insights and Elevate. You will join a mission-driven, collaborative team dedicated to delivering insights that enhance student success and institutional efficiency across Penn State. We are looking for a candidate with proven expertise across the entire data science lifecycle, from organizing complex institutional datasets to applying advanced analytical and modeling methods., * Extensive experience with the Python data stack (pandas, NumPy, etc.) for data cleaning, feature engineering, and high-level statistical modeling.

 * A robust background in quantitative methods, including regression, clustering, decision trees, and experimental design.
 * Expertise in writing complex SQL queries to extract and manipulate data within relational databases.
 * Proven ability to synthesize technical findings into clear, compelling, non-technical narratives for executive-level stakeholders.
 * Exceptional attention to detail and a commitment to the ethical use of data in the educational environment.
 * Experience with managing production-level data and code through version control and continuous integration/continuous deployment (CI/CD) workflows, such as GitHub Actions, or similar automation tools to manage the model lifecycle

Desired

 * Degree in Data Science, Computer Science, Information Science, or a related field.
 * Experience inheriting existing production level codebases or comfortable jumping into new codebase to debug, refactor, make improvements, etc.
 * Familiarity with R for supplementary statistical analysis or collaborative research scripts.
 * Experience with data visualization libraries (e.g., Plotly, Matplotlib) to create impactful visual stories.
 * Experience architecting and deploying RESTful APIs to serve machine learning models using frameworks such as FastAPI (Python) or Plumber (R).
 * Understanding of the Higher Education ecosystem, including FERPA compliance and the nuances of SIS and LMS data structures.

MINIMUM EDUCATION, WORK EXPERIENCE & REQUIRED CERTIFICATIONS
Master's Degree 6+ years of relevant experience; or an equivalent combination of education and experience accepted Required Certifications: None

BACKGROUND CHECKS/CLEARANCES
Employment with the University will require successful completion of background check(s) in accordance with University policies.

Penn State does not sponsor or take over sponsorship of a staff employment Visa. Applicants must be authorized to work in the U.S.

## Description

* Design, validate, and deploy predictive and descriptive models (e.g., student-at-risk indicators, and engagement patterns, etc) to inform institutional strategy.
 * Architect and maintain complex data workflows by retrieving and organizing institutional data from diverse sources, including Canvas (LMS), Starfish, and Student Information Systems (SIS).
 * Utilize exploratory data analysis and machine learning to transform raw data into high-level trends and actionable recommendations.
 * Collaborate with university leadership to move insights from theory to practice, ensuring findings are translated into intuitive, practical tools for faculty and advisors.
 * Communicate to technical and non-technical stakeholders and audiences on data and methodological questions.
 * Partner with software developers to transition successful models into production-level applications used by thousands of users across the university.