Lecturer (Business Digitalisation) – School of Business Management

• →Design and deliver Data Analytics and Business Analytics programmes covering data literacy, data visualisation, business intelligence, predictive analytics, AI applications, and modern data platforms using tools such as Excel, Power BI, Python, and SQL.
• →Teach predictive analytics and forecasting techniques, including machine learning, clustering, dimension reduction, and time-series analysis, using Python and industry-standard analytics libraries.
• →Guide learners in developing data-driven solutions and lightweight application prototypes while building business analysis and project management capabilities.
• →Integrate machine learning, generative AI, and agentic AI into teaching, enabling learners to apply AI technologies effectively, responsibly, and critically.
• →Develop and deliver continuing education and professional development programmes that strengthen analytics and AI capabilities for working professionals.
• →Design authentic learning activities and assessments that evaluate analytical reasoning, problem-solving, human judgment, and responsible AI usage.
• →Develop, review, and enhance curriculum, learning materials, laboratories, and assessments, including the adoption of Python-based tools and workflows across programmes.
• →Stay abreast of developments in analytics, AI, and emerging technologies, translating industry practices into relevant and current teaching content.
• →Work closely with academic teams, industry partners, and stakeholders to ensure curriculum relevance and alignment with workforce needs.
• →Provide student mentorship, pastoral care, career guidance, and internship support to nurture learners' professional growth.
• →Participate in outreach, industry engagement, and recruitment activities to promote data literacy, analytics, AI capabilities, and the School's programmes.
• →Support internationalisation efforts, including overseas learning programmes, study trips, and collaborative educational initiatives.
• →Teach and engage both full-time and adult learners, adapting pedagogical approaches to diverse learner profiles and experience levels.

• Relevant qualification in Data Science, Business Analytics, Statistics, Computer Science, Information Technology, or a related discipline (or Education with a strong analytics/technology focus).
• Minimum 3 years of relevant experience in analytics, training delivery, or professional development within technology, education, corporate, or public-sector contexts.
• Strong analytics capability, with experience in data preparation, analysis, modelling, and translating insights into business decisions.
• Proficiency in Python for analytics and machine learning, including predictive modelling, clustering, dimension reduction, forecasting, and model evaluation.
• Ability to develop and teach lightweight Python web applications with SQL database integration.
• Strong proficiency in Excel and business intelligence tools (e.g. Power BI, Tableau); exposure to modern data platforms such as Databricks is a plus.
• Working knowledge of machine learning, generative AI, and agentic AI, with the ability to teach concepts and guide responsible AI adoption.

• Familiarity with GenAI tools (e.g. ChatGPT, Claude, Microsoft Copilot) and AI ethics and governance principles.

• Experience with Microsoft Power Platform and Microsoft 365 technologies is an advantage.

• Ability to teach technical concepts to non-technical audiences and translate complex analytics and AI topics into practical learning experiences.

• Strong communication, stakeholder management, and collaboration skills.

Relevant experience in one or more of the following areas is preferred:

• End-to-end analytics projects, from problem framing and data preparation to analysis, visualisation, and insight communication.

• Designing or supervising AI-assisted or agentic analytical workflows

• Implementing business intelligence or automation solutions in an organisational setting

• Teaching or coaching with generative AI, including designing assessments that remain valid when learners have AI assistance
• Building a portfolio of analytics or application projects (e.g. GitHub repositories, published notebooks)