Senior Data Scientist & AI Engineer

Senior Data Scientist & AI Engineer page is loaded## Senior Data Scientist & AI Engineer locations:
Guttenberg Information Technologies Center time type:
Full time posted on:
Posted 30+ Days Agojob requisition #
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* Title:

** Senior Data Scientist & AI Engineer#
** Department:
** Director, Data Analytics#
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* Reports To:

** Director, Learning Technologies#
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* Position Summary:

** 1. Apply advanced statistical modeling, machine learning, and predictive analytics methods including time-series forecasting (ARIMA, Prophet, LSTM), survival analysis(Kaplan-Meier, Cox regression), and optimization techniques to institutional data requiring domain knowledge of admissions, financial aid, enrollment, and student success data to support university-wide analytical initiatives and operational efficiency.
2. Design, implement, and operationalize automated end-to-end statistical and machine learning workflows using Python, R, and Data Robot, integrating Snowflake via Data Robot REST APIs and custom Streamlit applications to automate model training,scoring, validation, monitoring, and controlled production deployment.
3. Architect, build, and deploy production-grade AI agent-based decision-support systems (including model configuration and fine-tuning) that enable faculty and administrators toquery, explore, and interpret governed institutional data using natural language, by developing large language model (LLM)-powered agents and retrieval-augmented generation (RAG) pipelines integrated with Snowflake and enterprise data sources to support university-wide analytics.
4. Integrate, manage and process data from multiple higher-education-related data systems, including Banner (direct or through Cognos), Slate, Common Application (Common App), and Workday, as well as external higher-education datasets including IPEDS and National Student Clearinghouse.
5. Identify, define, and validate analytical attributes, measures, dimensions, and derived metrics within enterprise and external higher-education datasets by designing and maintaining analytically meaningful data models and semantic layers (e.g., fact and dimension structures) that translate raw institutional data into consistent, reporting-ready structures supporting statistical analysis, machine learning, AI development, and institutional planning.
6. Respond to data requests by writing and optimizing complex SQL queries and Snowpark python scripts to extract, join, aggregate, and validate large-scale institutional datasets stored in the Snowflake Data Warehouse, including development of custom, reusable analytical views integrating cross-departmental data.
7. Oversee and perform data extraction, transformation, feature engineering, and validation using SQL, Python, R, and Snowpark to build and maintain scalable data pipelines that prepare structured and unstructured data for modeling and AI applications.
8. Collaborate with Data Governance stakeholders to define analytical requirements and support accurate, governed institutional data; contribute to documentation and validation of business and technical data definitions using Data Cookbook, ensuring consistency, reproducibility, and compliance within institutional analytics.
9. Certify dashboards and metrics through reviews of the underlying data, mathematical assumptions, transformations, and calculations applied; formally approve dashboards and ensure appropriate access controls and security for trusted reporting.
10. Design, deploy, and automate interactive dashboards, analytical workflows, and self-service analytics products using Strategy (Micro Strategy) and Snowflake Warehouse through an iterative development process of gathering stakeholder requirements and incorporating feedback to operationalize institutional metrics and AI-generated insights, and communicate complex analytical findings to support actionable decision-making.
11. Establish and maintain version control and model governance best practices using Git and related tools to manage SQL code, analytical scripts, AI models, dashboards, and documentation across development and production environments; coordinate analytics and data project workflows using Service Now utilizing Agile/SCRUM methodologies toensure reproducibility, collaboration, and controlled deployment.
12. Mentor graduate students and junior data scientists, leading technical ideation for strategic projects involving mathematical modeling, machine learning model development, and design of statistical hypothesis tests (e.g.,…