Duties and Responsibilities:

Design, develop, validate, and deploy machine learning models to improve operational efficiency, decision-making, and student success outcomes.
Build predictive models and intelligent decision-support tools for use cases such as enrollment marketing, student advising, course engagement, persistence, and retention.
Develop and implement LLM-powered solutions by leveraging popular LLM APIs for university stakeholders.
Evaluate emerging AI and machine learning technologies and recommend practical adoption strategies aligned with university goals, governance, and responsible AI principles.
Partners with leaders and subject matter experts across marketing, advising, academic affairs, teaching and learning, and student success to identify high-impact opportunities for analytics and automation.
Translate complex business problems into data science solutions, including experimentation, feature engineering, model development, and performance monitoring.
Collaborate with IT teams to productionize models, integrate solutions into workflows, and maintain scalable, reliable data products.
Communicate insights, model results, and recommendations clearly to technical and non-technical audiences through presentations, dashboards, and written documentation.

Qualifications:

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Master's degree in data science, Computer Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field. 2-3 years experience of data scientist or analyst. Demonstrated experience developing and implementing machine learning models in production environments. Strong proficiency in Python, SQL, and common machine learning frameworks and libraries. Experience with end-to-end model lifecycle management, including data preparation, feature engineering, training, validation, deployment, and monitoring. Hands-on experience with LLMs, natural language processing, prompt design, evaluation, and/or generative AI applications. Familiarity with cloud platforms and modern data science tooling for scalable model development and deployment. Strong analytical, problem-solving, and communication skills, with the ability to influence decisions through data-driven insights. Ability to work cross-functionally and manage multiple priorities in a collaborative environment. The hiring salary range for this position is $85,000 - $95,000. 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