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

Knoxville, TN, United States

University of Tennessee

With a presence in each of Tennessee’s 95 counties, the University of Tennessee System carries out its education, research and outreach mission every day.

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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.

Preferred Qualifications

  • Education: Master's degree in Data Science, Computer Science, Statistics, Engineering, Mathematics, or a related discipline.
  • Experience:

Work Location 

  • Knoxville, TN. This position does have the potential for a hybrid working capacity.

Compensation and Benefits 

  • UT market range: MR 12
  • Hiring Range: $65,000-85,000/year dependent on experience
  • Find more information on the UT Market Range structure here
  • Find more information on UT Benefits here

 

Application Instructions 

  • For full consideration, applicants must attach a letter of interest, resume, and the name, address, email, and phone number of three professional references, in addition to completing the applicant file to the Human Resources online application system, Taleo. This position does have the potential for a hybrid working capacity.
  • Screening of applicants will begin immediately and continue until the position has been filled. DL_UT

 

About The College/Department/Division 

Digital Learning at UT, is a unit charged with advancing UT’s commitment to discovery, creativity, learning, and engagement, specifically for online learners. Using advanced and innovative technologies, Digital Learning at UT will provide scalable solutions that enhance the online learning experience and reduce barriers to student access and success, providing a tremendous impact to online learners across the state and the nation. At Digital Learning, we want to be bold and impactful, transforming the future of online education through innovative thinking and collaborative problem-solving.  Join our dynamic and inclusive Digital Learning team where we take pride in teamwork, excellence, and a shared commitment to shaping the future through education and innovation.

The University of Tennessee, Knoxville, has shaped leaders, changemakers, and innovative thinkers since its founding in 1794. The university is home to more than 38,000 students and 10,000 statewide employees—the Volunteers—who uphold the university’s tradition of lighting the way for others through leadership and service. 

UT Knoxville offers over 900 programs of study across 14 degree-granting colleges and schools. As Tennessee’s flagship land-grant university, its footprint spans the entire state. The university holds the highest Carnegie classification for research activity and has deep partnerships with industry leaders and the US Department of Energy’s largest multidisciplinary laboratory, Oak Ridge National Laboratory. 

The Knoxville campus serves and recruits for UT Knoxville, including the Institute of Agriculture and the Space Institute, as well as the UT Institute of Public Service.  

UT Knoxville considers its employees its number one asset. With values that focus on work-life balance, compensation, and innovation leadership, all Vols are supported to advance professionally. Employees have access to career development and coaching, continued education, and an extensive list of development and training possibilities. The Volunteer employee experience implements structures and practices that attract and retain a diverse community and that support a culture where everyone matters and belongs.  

The university holds a strong commitment to its land-grant mission of learning and engagement, with a tradition of service and leadership that carries that Volunteer spirit throughout the state and around the world. It has been ranked nationally as “Best Employer for New Graduates,” “One of America’s Best Large Employers,” and “Best Workplace for Women,” and has been designated as “Best Place for Working Parents” by Forbes Magazine.  

Apply today and join the Tennessee Volunteer community!

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Tags: AWS Azure Classification Clustering Computer Science Data analysis Data Analytics Data Mining Data visualization Deep Learning Engineering GCP Google Cloud Keras Machine Learning Mathematics Matplotlib Power BI Python RDBMS Research Salesforce SQL Statistical modeling Statistics Tableau TensorFlow

Perks/benefits: Career development

Region: North America
Country: United States

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