Data and AI Automation Specialist

The Data and AI Automation Specialist is embedded within the Division of Computing and Mathematical Sciences (CMS) and holds two closely connected responsibilities. The first is to transform academic, student, faculty, research, and operational data into reliable evidence for divisional planning and decision making. The second is to design, build, and maintain the automated and agentic AI workflows that put that evidence, and other routine university processes, to work.

The role works closely with CMS leadership, faculty, program administrators, and professional staff to collect, validate, analyze, interpret, and communicate information that supports teaching quality, student success, program development, research performance, resource planning, accreditation, and continuous improvement. This is a hands‑on building role as well as an analytical one: the specialist is expected to construct, test, document, and operate working automations and agentic AI solutions in low code platforms such as KNIME and n8n, rather than only specifying them for others to build.

The specialist will work in close partnership with Institutional Research to align definitions, methods, governance, and reporting with university standards and authoritative data sources, and with Information Technology on integration, access, and safe deployment. The role also serves as the business analysis partner for faculty led AI initiatives that support university operations, translating operational needs into clear requirements, identifying suitable and authoritative data sources, assessing data quality and readiness, and providing analytical and practical AI support throughout solution development and evaluation.

• Collect, integrate, clean, validate, and analyze quantitative and qualitative data relevant to CMS programs, courses, students, faculty, research, and operations.
• Analyze curriculum delivery, course demand, teaching activity, student outcomes, faculty workload, research productivity, and resource utilization to provide evidence for curriculum planning, course scheduling, student success initiatives, faculty and resource planning, program review, and continuous improvement.
• Apply appropriate statistical methods, trend analysis, forecasting, segmentation, and predictive techniques to identify patterns, risks, and opportunities within CMS.
• Investigate data anomalies, reconcile conflicting sources with relevant data owners and Institutional Research, and document assumptions and limitations before results are released.

• Develop and maintain recurring CMS reports, profiles, scorecards, and interactive dashboards for the Division leadership, committees, faculty, and authorized university stakeholders.
• Translate complex academic, research, and operational findings into concise narratives, visualizations, presentations, and recommendations for technical and nontechnical audiences, highlighting material changes, risks, emerging issues, and implications for leadership and committee decision‑making.
• Respond to approved CMS information requests using clear definitions, reproducible methods, appropriate access controls, and documented quality checks.
• Automate repeatable data preparation and reporting activities to improve the timeliness, consistency, and scalability of CMS analytics.

• Compile and verify CMS evidence for program accreditation, academic program review, curriculum review, and quality assurance activities.
• Maintain reporting calendars, supporting documentation, audit trails, and source records for CMS data products and submissions.
• Interpret academic and external reporting requirements with Institutional Research, identify CMS data gaps, and work with relevant owners to resolve them.

• Benchmark CMS programs, disciplines, and performance against relevant local and international peers using comparable definitions and defensible indicators.
• Research developments and good practice in academic analytics, computing and mathematical sciences education, research performance, and data management.
• Support the design, monitoring, and review of divisional key performance indicators and measures aligned with CMS and University priorities.

• Serve as the primary analytical liaison between CMS and Institutional…