Assistant Professor of Business Analytics

Analytics is not a single course at UKB; it is the spine of our curriculum. Our graduates are promised evidence of what they can do, and in analytics that means working with real data, real tools and real business questions. We need a faculty member who can teach quantitative and analytics courses with rigor and accessibility. Our promise is that every graduate leaves with evidence of what they can do, not only what they know

• Business Statistics, Quantitative Business Analysis, Business Analytics, Data Visualisation, AI and its Applications, and analytics electives. Use capability-based assessment (live projects, certifications, portfolios) and current tools (Python or R, SQL, Excel at advanced level, Power BI or Tableau, and at least one machine-learning workflow; generative AI tools for analysis and reporting). Supervise capstones and internships.
• Sustain a Scopus Q1 publication pipeline in the discipline; pursue external and industry-funded research.
• Embed analytics learning outcomes into existing programmes, map CLOs to programme competencies, run Assurance of Learning cycles, and prepare course files to CAA and AACSB standards.
• Build partnerships for guest lectures, internships and applied research (banks, telecoms, retailers, ports and logistics operators, government data units and technology firms); serve on College and University committees.

• PhD in Business Analytics, Management Science, Operations Research, Information Systems, Statistics or a closely related field with a business-analytics dissertation, from an accredited institution recognised by the UAE Ministry of Higher Education and Scientific Research; with a minimum of one year of university teaching experience after the PhD.
• Scopus h-index of 5 or above. Include your Scopus author profile link in the CV.
• Evidence of effective university teaching in the discipline; fluent English (Arabic an asset).
• Candidates with industry experience in business analytics are preferable, or candidates with evidence of having collaborated with industry (joint projects, consulting, executive training, internships, advisory work).
• Knowledge of how programmes are developed: writing course and programme learning outcomes, assessment design, competency mapping, study plans.
• Familiar with accreditation (CAA, AACSB, EQUIS, AMBA or ACBSP) and understands how the process works: standards, Assurance of Learning, self-study and evidence files.
• Professional certification in the field is preferable, e.g. Microsoft Power BI Data Analyst, Tableau, AWS/Azure/Google data or ML certifications, SAS, or equivalent.