Jobs and Careers

Machine Learning & AI Research Analyst - Digital Learning at UT - UTK

United Statesfull_timeVerifiedPosted 29 Apr 2025

💰 $85,000/yr($65,000/yr – $85,000/yr)

About the role

Supports the Digital Learning division by analyzing large student datasets to identify factors influencing enrollment, retention, graduation rates, and dropout rates. The ideal candidate will have a strong background in Python programming, proven experience in data analysis, machine learning, and statistical modeling. Responsible for linking data from multiple internal sources as well as importing and linking data from external sources. Integrates concepts and data from various sources such as Google Analytics, Salesforce, CPD platforms, and internal systems such as Slate and Banner to perform predictive analysis for student success. Excellent communication skills are essential for translating complex data insights into actionable recommendations for improving student outcomes and informing decisions at the highest levels of the division.

Data Collection & Preparation

Collaborate with relevant teams to gather and clean marketing, admissions, enrollment, academic program and other student data from various sources. This includes handling missing values, encoding categorical variables, and preparing data for analysis.
Prepare datasets for analysis by handling missing values, encoding categorical variables, and ensuring overall data readiness

Data Analytics & Modeling

Conduct in-depth analysis of student data using statistical methods and machine learning algorithms (regression, classification, clustering) to identify trends, patterns, and key factors related to student success.
Identifying Success Factors: Employ association rule mining and other techniques to uncover relationships between student behaviors, demographics, program characteristics, and success outcomes.

Predictive Analytics & AI/ML

Build predictive models to forecast student enrollment, dropout, graduation, and retention. This involves selecting appropriate algorithms, training models, and evaluating their performance.
Student Segmentation: Utilize clustering techniques to segment students into groups based on shared characteristics to enable targeted interventions.

Reporting & Communication

Prepare clear and concise reports summarizing findings, including visualizations and actionable recommendations for improving student success. Organizes results and conclusions of data analysis for use in publications and presentation by departmental leaders.

Team Collaboration & Professional Development

Work closely with cross-functional teams to translate data insights into practical strategies for improving enrollment, retention, and graduation rates. Furthermore, collaborate with data analysts in the data science team to ensure data integrity and adhere to data ethical standards.
Stay up to date with the latest developments in data analysis and machine learning techniques and apply them to improve the accuracy and effectiveness of student success models.

Required Qualifications

Education: Bachelor’s degree in Data Science, Computer Science, Statistics, Engineering, Mathematics, or a related discipline.
Experience: Two (2) or more years of experience in data analysis, data mining, data modeling, machine learning, or a related role.
Applicants must be legally authorized to work in the United States on a full-time basis without need now or in the future for sponsorship for employment visa status.
- Experience with deep learning frameworks like TensorFlow or Keras, SynapseML/MMLSpark.
- Experience with SQL and relational databases.
- Experience in data visualization tools (Power BI, Matplotlib, Tableau).
- Familiarity with cloud computing and data analytics platforms (Microsoft Fabric, AWS, Google Cloud, Azure).
- Experience working with student data in the higher education domain.
- Experience with association rule mining libraries like mlxtend.