New opportunity

Lecturer (Business Digitalisation) – School of Business Management

Nanyang Polytechnic · Singapore

About this role

The Lecturer (Business Digitalisation) at Nanyang Polytechnic will design and deliver comprehensive programmes in Data and Business Analytics. The role focuses on teaching students to think critically alongside AI tools, balancing human judgment with machine capabilities. Responsibilities include: Curriculum Development: Design and enhance curriculum, learning materials, and assessments covering data literacy, business intelligence, predictive analytics, and AI applications. Teaching: Deliver instruction on predictive analytics, forecasting, machine learning, and lightweight application development using Python and SQL. AI Integration: Incorporate generative AI and agentic AI into teaching, ensuring students apply these technologies responsibly and critically. Student Support: Provide mentorship, career guidance, and internship support. Industry Engagement: Collaborate with academic teams and industry partners to ensure curriculum relevance and participate in outreach activities. Qualifications: A relevant degree in Data Science, Business Analytics, Statistics, Computer Science, IT, or Education with a strong analytics focus. Minimum 3 years of relevant experience in analytics, training, or professional development. Proficiency in Python, Excel, and BI tools (Power BI, Tableau) is required. Knowledge of machine learning, GenAI, and AI ethics is essential. Preferred experience includes end-to-end analytics projects, designing AI-assisted workflows, implementing business intelligence solutions, and building project portfolios.

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Skills for this role

Data AnalyticsBusiness AnalyticsPythonSQLExcelPower BITableauMachine LearningGenerative AIAI AgentsPredictive AnalyticsData VisualizationForecastingClusteringDimension ReductionDatabricksMicrosoft Power PlatformMicrosoft 365AI EthicsAI Governance

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Curriculum Design for AI-Augmented Analytics
The role requires developing and updating learning materials that integrate predictive analytics, machine learning, and generative AI. You must demonstrate the ability to translate complex technical concepts into practical, industry-relevant curricula that teach students how to balance machine outputs with human judgment.
Instructional Delivery for Diverse Learner Profiles
You will be responsible for teaching both full-time students and adult learners. Success depends on your ability to adapt pedagogical approaches to different experience levels while maintaining high standards in technical subjects like Python, SQL, and BI tools.
Industry-Aligned Practical Training
The position emphasizes bridging the gap between academic theory and workforce needs. You must collaborate with industry partners to ensure that projects, internships, and outreach activities remain current with modern data platforms and business intelligence trends.

Likely challenges to think through

Designing AI-Resilient Assessments
A significant challenge is creating assessments that accurately evaluate analytical reasoning when students have access to generative AI. Prepare by researching methods to design 'authentic' learning activities that test critical thinking and the ability to override or validate AI-generated outputs rather than just producing a final result.
Balancing Technical Depth with Accessibility
You must teach advanced topics like agentic AI and predictive modeling to non-technical audiences. Prepare by practicing how to explain the 'why' behind technical workflows and demonstrating how to simplify complex data science concepts without losing the rigor required for professional application.

Practice your answers

Technical practice

Teaching Predictive Analytics and Machine Learning

What to coverExplain how you would introduce students to predictive modelling, clustering, and dimension reduction using Python. Focus on your ability to explain the underlying logic of these algorithms and how to evaluate model performance effectively.

Integrating Generative and Agentic AI into Curriculum

What to coverDescribe how you would incorporate GenAI and agentic AI tools into a business analytics course. Discuss how you ensure students use these tools critically and responsibly rather than relying on them for automated answers.

Developing Lightweight Python Applications with SQL

What to coverDiscuss your approach to teaching students how to build data-driven prototypes that integrate with SQL databases. Focus on the architecture of simple web applications and how to guide students through the end-to-end development process.

Designing Assessments in the Age of AI

What to coverHow do you design authentic learning activities and assessments that remain valid when students have access to generative AI? Suggest methods for evaluating analytical reasoning and human judgment rather than just the final output.

Business Intelligence and Data Visualization

What to coverCompare the use cases for Power BI and Tableau in a business context. Explain how you would teach students to select the right visualization techniques to translate complex data into actionable business insights.

AI Ethics and Governance in Practice

What to coverWhat are the core principles of AI ethics you would emphasize to students? Discuss how you would guide them to identify bias, ensure transparency, and understand the implications of AI-driven decision-making in an organizational setting.

End-to-End Analytics Project Workflow

What to coverDescribe the stages of an end-to-end analytics project from problem framing to insight communication. Suggest how you would mentor students to maintain rigor throughout the data preparation and modeling phases.

Adapting Pedagogical Approaches

What to coverHow do you adjust your teaching style when moving between full-time polytechnic students and adult learners? Suggest strategies for making complex technical concepts accessible to non-technical audiences.

Behavioural practice

Collaborating with Industry Partners

What to coverDescribe a situation where you worked with industry stakeholders to ensure the relevance of a curriculum or project. Focus on how you identified their needs and translated those requirements into actionable learning outcomes.

Mentoring and Student Support

What to coverProvide an example of how you have provided pastoral care or career guidance to a student struggling with technical content. Explain the actions you took to support their professional growth and the resulting impact on their performance.

Handling Disagreement on AI Outputs

What to coverShare an experience where you had to guide a learner or colleague to critically evaluate and override an AI-generated output. Detail the situation, your reasoning process, and how you helped them understand the importance of human judgment.

Managing Stakeholder Expectations

What to coverDescribe a time you had to explain a complex technical or AI-related concept to a non-technical stakeholder. Explain how you simplified the information while maintaining accuracy and the outcome of that communication.

Sharpen your knowledge

Topics to prepare

Responsible AI and Ethics in Education
Review principles of AI governance and ethics to prepare for teaching students how to critically evaluate AI outputs. Exercise: Draft a lesson plan that includes a 'human-in-the-loop' activity where students must identify and correct a hallucination or bias in a generative AI response.
Pedagogical Design for AI-Assisted Learning
Focus on designing assessments that remain valid in an era of AI assistance. Exercise: Create an assessment rubric that evaluates the student's analytical reasoning and process documentation rather than just the final output, ensuring it accounts for the use of AI tools.
Agentic AI Workflows
Understand the conceptual framework of agentic AI, where AI systems perform tasks autonomously. Exercise: Map out a simple business workflow that could be automated using an AI agent, identifying the specific decision points where human oversight is required.

Skills to revise

Python for Predictive Analytics
Demonstrate proficiency in machine learning libraries for forecasting and modeling. Exercise: Build a small project using a public dataset to perform time-series forecasting or clustering, documenting the code in a Jupyter notebook to showcase your technical workflow.
Lightweight Application Development
Develop skills in creating simple web applications that integrate with SQL databases. Exercise: Create a basic Python-based web app (e.g., using Streamlit or Flask) that queries a SQL database to display a business metric, reflecting the requirement to teach students practical application building.
Business Intelligence Tooling
Ensure high proficiency in Power BI or Tableau for data visualization. Exercise: Develop an end-to-end dashboard using a sample dataset that translates raw data into actionable business insights, focusing on clear communication for non-technical stakeholders.

Your preparation checklist

Curate a portfolio of end-to-end analytics projects
Prepare a collection of projects that demonstrate your ability to frame business problems, prepare data, perform modeling, and communicate insights. Include GitHub repositories or published notebooks that showcase your proficiency in Python, SQL, and BI tools like Power BI or Tableau.
Develop a teaching demonstration on AI-assisted workflows
Prepare a short lesson plan or presentation that explains a complex analytics concept to a non-technical audience. Incorporate how you would guide students to use generative AI or agentic AI tools responsibly, emphasizing critical thinking and human judgment over simple automation.
Draft an assessment strategy for an AI-integrated curriculum
Create a sample assessment design that remains valid even when students have access to generative AI. Explain how you would evaluate a student's analytical reasoning and problem-solving skills rather than just the final output produced by an AI tool.
Prepare examples of lightweight application development
Be ready to discuss or demonstrate your experience in building lightweight Python applications integrated with SQL databases. Highlight projects where you translated business requirements into functional, automated, or data-driven solutions.
Formulate questions regarding industry engagement and curriculum relevance
Prepare thoughtful questions for the interviewers about how the School of Business Management collaborates with industry partners to keep the curriculum current. Ask about the specific challenges of balancing academic rigor with the rapid evolution of AI and data technologies.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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