Job Details
Application Open: Full-Time
Job Purpose:
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.
Key Responsibilities:
Academic and divisional data analysis
- 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.
Divisional reporting, dashboards, and decision support
- 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.
Program quality, accreditation, and university reporting
- 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.
Academic planning and performance improvement
- 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.
Data governance and cross university collaboration
- Serve as the primary analytical liaison between CMS and Institutional Research, coordinating with Institutional Research and other University offices to ensure accurate and timely CMS data for regulatory submissions, rankings, surveys, approved external reporting, and other institutional requirements.
- Maintain CMS data dictionaries, metric definitions, reporting specifications, business rules, and standard operating procedures in alignment with university standards.
- Apply university policies and applicable requirements for privacy, confidentiality, access control, retention, and the ethical use of academic and research data, including the use of third-party AI services.
- Promote consistent use of authoritative data sources and collaborate with Institutional Research and Information Technology to strengthen data quality, system design, and reporting practices.
- Build trusted relationships across CMS and provide practical guidance to faculty and staff on interpreting and using divisional data and AI enabled tools.
Workflow automation and agentic AI development
- Design, build, test, document, and maintain end to end data and process automations using low code workflow platforms such as KNIME and n8n, covering scheduled data pipelines, validation routines, alerting, and the preparation and distribution of recurring reports.
- Build and operate agentic AI workflows that combine large language models with university data sources, APIs, and tools to support tasks such as document summarization, information retrieval, classification and routing, drafting, extraction of structured data from unstructured sources, and first line handling of routine requests.
- Integrate workflows with university systems and services through APIs, webhooks, databases, file stores, email, and collaboration platforms, working with Information Technology on authentication, permissions, environments, and safe deployment.
- Apply sound engineering practice to automation, including version control, separation of development and production environments, parameterization and reuse, error handling, retry and fallback logic, logging, and monitoring.
- Define and run evaluation for AI enabled workflows, including representative test sets, accuracy and quality measures, failure analysis, cost and latency tracking, and regression testing before and after changes.
- Design human in the loop controls, approval steps, escalation paths, and explicit boundaries for autonomous action, so that agents operate only within approved limits and failures are detected early.
- Maintain a register of divisional automations and agents for recording purpose, owner, data used, systems touched, dependencies, review dates, and decommissioning plans.
- Document the data flow of each automation and agent, including which data leaves university systems, where it is processed, what is logged, and how long it is retained, and raise any concerns before deployment.
- Prototype quickly, demonstrate working solutions to stakeholders, advance the ones that prove their value from pilot to supported operation, and retire those that do not.
- Provide practical enablement to CMS faculty and staff on approved AI tools and automation practice through guidance, templates, reusable components, and short training sessions.
Business analysis and AI enabled operational support
- Partner with CMS faculty and operational stakeholders to identify, clarify, and prioritize opportunities where AI-enabled tools can improve University processes and services.
- Act as the business analysis bridge between faculty, technical teams, and operational stakeholders for AI-enabled initiatives by eliciting stakeholder needs, mapping current processes, defining functional and data requirements, documenting use cases, establishing measurable acceptance criteria, and ensuring solutions remain aligned with business needs, University policies, data governance requirements, and agreed success measures.
- Identify and assess authoritative internal and external data sources required for AI solutions, evaluating accessibility, relevance, completeness, consistency, lineage, privacy constraints, and fitness for purpose; profile and validate the data, document quality and remediation requirements, and coordinate with Institutional Research, Information Technology, data owners, and faculty to prepare reliable datasets for development and testing.
- Support the design, prototyping, testing, evaluation, and responsible adoption of agentic AI tools, including validation of outputs, workflow behavior, data use, and operational performance.
- Develop supporting documentation, process maps, requirements specifications, data inventories, user guidance, and performance reports for AI-enabled initiatives.
Other Duties
- Contribute to university-wide programs, including orientation, wellbeing initiatives, global observances, leadership development, and personal growth activities.
- Perform all other duties as reasonably directed by the line manager that are commensurate with these functional objectives.
Qualifications and Criteria:
- Bachelor's degree in statistics, data science, mathematics, computer science, information systems, business analytics, or related quantitative discipline.
- Master's degree in a relevant quantitative, computing, mathematical, educational, or management discipline will be preferred.
Essential :
- Minimum of six years of relevant experience in data analysis, academic analytics, business intelligence, institutional research, research analytics, or a closely related field, including at least two years spent building workflow automation or AI enabled solutions.
- Demonstrated hands on experience designing, building, and operating automated workflows in a low code automation or data science platform such as KNIME or n8n, or a directly comparable tool such as Alteryx, Make, Zapier, Microsoft Power Automate, Dify, Flowise, or Apache Airflow. Candidates should be able to walk through workflows they personally built and maintained.
- Practical experience building or configuring agentic AI workflows, meaning solutions in which a large language model uses tools, data sources, or multi step reasoning to complete a task, together with the ability to explain how the solution was tested, controlled, and handed over to users.
- Practical comfort working with APIs, JSON, webhooks, scheduling, and authentication methods sufficient to connect systems reliably without full software engineering support.
- Demonstrated experience preparing accurate reports and dashboards from multiple data sources and explaining findings to academic and professional stakeholders.
- Proficiency in SQL and working proficiency in Python for data preparation, analysis, and automation, together with strong spreadsheet analysis skills.
- Experience with a visualization platform such as Power BI or Tableau.
- Professional fluency in English, with strong written, presentation, and interpersonal communication skills.
- Demonstrated ability to elicit, analyze, and document business, functional, process, and data requirements in collaboration with technical and nontechnical stakeholders.
- Working knowledge of applied AI concepts, including large language models, generative AI, prompt design, retrieval augmented generation, structured output, tool and function calling, agent orchestration, and common failure modes such as hallucination, prompt injection, and silent degradation over time.
Preferred :
- Experience in a university or research-intensive environment, preferably supporting an academic division, college, department, or academic program.
- Familiarity with student information systems, learning management systems, research information systems, data warehouses, survey platforms, and automated reporting workflows.
- Knowledge of higher education data standards, academic quality processes, and the UAE higher education context.
- Experience analyzing student outcomes, curriculum delivery, faculty activity, research productivity, or graduate education in computing, mathematics, or artificial intelligence disciplines.
- Experience supporting the development or implementation of AI enabled operational tools, workflow automation, decision support solutions, or intelligent agents.
- Experience with agent and AI development frameworks or platforms such as LangChain, LangGraph, LlamaIndex, Microsoft Copilot Studio, Azure AI Foundry, Amazon Bedrock, or the developer platforms of major model providers.
- Experience with embeddings, vector search, and retrieval design for document heavy or knowledge base use cases.
- Familiarity with version control using Git, containerization, environment management, and basic deployment or CI/CD practice.
- Relevant certification in an automation or analytics platform, for example KNIME, Alteryx, Power Platform, or a recognized AI engineering credential.
- Exposure to AI governance, assurance, or risk management frameworks and their application to operational AI systems.
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