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.
