New opportunity

Adjunct Faculty Data Science (Azure AI) - Singapore

EduCLaaS · Geylang Road, Singapore

About this role

EduCLaaS seeks an adjunct faculty member to deliver data science and Azure AI learning in Geylang Road, Singapore. The contract role supports learners through blended learning, subject-matter mentoring and training tailored to their needs.

Responsibilities

Deliver courses under EduCLaaS learning delivery, operations and technology policies, with a focus on learner satisfaction and successful skills outcomes.
Manage cohorts and programmes within time, budget and P&L requirements; use data analytics to assess programme profitability.
Assess learners’ training needs and adjust delivery accordingly.

Required qualifications and capabilities:

Bachelor’s degree, proven experience as a facilitator, speaker or coach, and 5+ years of experience in data science and machine learning.
Extensive programme management experience; familiarity with education or university frameworks, financial and risk management, and educational technology implementation.
Working knowledge of R, SQL, Python, machine learning, AI, Spark and statistics; Python packages including NumPy, Pandas, scikit-learn, Seaborn and Matplotlib; BI tools such as Tableau and Power BI; and data frameworks such as Hadoop and Spark.
Strong database skills, including SQL, data preparation, cleaning and wrangling. Strong communication, empathy and the ability to adapt instruction are required.

Preferred qualifications and traits: Azure Machine Learning knowledge, particularly its data and ML stack, is a plus. Microsoft Power BI or Microsoft Machine Learning certification is good to have. English competency is desirable because classes are conducted in English, as are interpersonal and organizational skills and a nurturing, proactive and accountable approach.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Data ScienceMachine LearningAIRSQLPythonApache SparkStatisticsNumPyPandasscikit-learnSeabornMatplotlibTableauPower BIHadoopAzure Machine LearningData PreparationData CleaningData WranglingProgramme ManagementFinancial ManagementRisk ManagementEducational TechnologyFacilitationCoachingCommunication

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

Adapt teaching to learner needs
The role supports blended learning, mentors students and calls for assessing training needs and adjusting delivery. Prepare a facilitation example that shows how you identified a learning gap, changed your approach and checked whether learners progressed.
Teach applied data science clearly
The requirements span data preparation, SQL, Python and R, statistics, machine learning, AI, Spark and BI tools. Prepare to explain a practical workflow from cleaning data through analysis or modelling to communicating results, at a level learners can follow.
Run courses within delivery standards
EduCLaaS expects courses to follow its learning-delivery, operations and technology policies, with attention to learner satisfaction and skills outcomes. Prepare examples of managing a programme and using educational technology while maintaining a consistent learner experience.
Manage cohort economics
The role is responsible for cohorts and programmes within time, budget and P&L requirements, using analytics to assess profitability. Prepare to describe how you have tracked programme performance and made decisions using financial and learner-outcome information.

Likely challenges to think through

Personalization versus cohort pace
Potential challenge: learners may need different levels of support while a cohort still has a schedule. Prepare an example of adapting instruction without losing sight of shared skills outcomes.
Breadth versus teaching depth
Potential challenge: the stated technical range is wide, while learners need coherent explanations rather than a tour of tools. Prepare a focused teaching example that connects core data-science steps; treat Azure Machine Learning as a plus, not a stated requirement.
Learner outcomes versus programme constraints
Potential challenge: improving learner support may consume time or budget that the role must manage. Prepare to discuss how you would evaluate that trade-off using learner progress, satisfaction and programme financial data, without assuming a particular EduCLaaS process.

Practice your answers

Technical practice

How would you teach learners to clean and join messy data using SQL?

What to coverThe posting emphasizes database skills, SQL, data preparation and wrangling. Suggested preparation: explain how you would check missing values, duplicates and join keys, then help learners verify that the resulting dataset is fit for analysis.

How would you explain model evaluation to learners with different statistical backgrounds?

What to coverThe role calls for statistics, machine learning and adaptable instruction. Suggested preparation: compare suitable evaluation measures, data splits and the risk of leakage, while explaining how you would adjust the depth of instruction.

How would you structure a practical Python machine-learning lesson?

What to coverThe posting names NumPy, Pandas, scikit-learn, Seaborn and Matplotlib. Suggested preparation: outline a teachable progression from data exploration to model fitting and visualization, including checks that learners understand each step.

When would you choose R, Python or SQL for a data-science learning exercise?

What to coverWorking knowledge of all three is listed in the posting. Suggested preparation: discuss the task each language would serve, the learners' starting skills and the trade-off between introducing a new tool and achieving the lesson objective.

How would you explain when Spark is useful instead of a smaller-scale workflow?

What to coverThe posting lists Spark and Hadoop among its data frameworks. Suggested preparation: contrast distributed processing with a simpler approach in terms of data scale, complexity and what learners need to understand before using it.

How would you teach learners to assess a Tableau or Power BI dashboard?

What to coverExperience with Tableau or Power BI is listed. Suggested preparation: address whether the measures, visual choices and filters communicate a reliable finding, and how you would help learners spot misleading displays.

How would you introduce an Azure Machine Learning exercise without obscuring the underlying ML concepts?

What to coverAzure Machine Learning knowledge, especially its data and ML stack, is described as a plus rather than a requirement. Suggested preparation: explain how you would connect any platform exercise to data preparation, training and evaluation without assuming a particular Azure workflow.

How would you use educational technology in a blended data-science course?

What to coverThe role supports blended learning and calls for understanding educational technology implementation. Suggested preparation: describe how you would combine independent practice, live instruction and feedback, and how you would check whether learners are progressing.

How would you adapt a data-science lesson while maintaining programme requirements?

What to coverThe posting asks for familiarity with education or university frameworks, adherence to learning-delivery policies and assessment of learner needs. Suggested preparation: discuss how you would identify a knowledge gap, adjust the explanation or exercise, and keep the intended skills outcome in view.

How would you assess a cohort's profitability without overlooking delivery risks?

What to coverThe role includes time, budget and P&L responsibility, profitability analysis and risk management. Suggested preparation: identify the revenue, cost and delivery measures you would examine, then discuss trade-offs between financial performance and learner outcomes.

Behavioural practice

Tell me about a time you changed your teaching approach after identifying a learner need.

What to coverThe posting calls for assessing training needs and adapting delivery. Suggested preparation: use a real situation, describe how you identified the need and what you changed, then state the observable result.

Describe a time you mentored someone through a difficult technical concept.

What to coverSubject-matter mentoring, empathy and communication are central to the role. Suggested preparation: explain the learner's challenge, the support you provided and how you determined whether it helped.

Tell me about a time you managed a programme under time or budget pressure.

What to coverThe posting assigns cohort and programme delivery within time and budget constraints. Suggested preparation: set out the constraint, the decisions and coordination you handled, and the result for delivery.

Describe a time you identified and addressed a risk to a learning programme.

What to coverRisk management and learning-operations responsibilities are listed. Suggested preparation: give a real situation, explain how you assessed and addressed the risk, and state what happened.

Tell me about a time you balanced learner feedback with a required delivery process.

What to coverThe role seeks learner satisfaction while requiring courses to follow learning-delivery and technology policies. Suggested preparation: describe the feedback, the actions you took within the applicable process and the outcome, without claiming knowledge of EduCLaaS's internal procedures.

Sharpen your knowledge

Topics to prepare

Statistics, machine learning and AI concepts
Required working knowledge includes statistics, machine learning and AI. Revise by explaining how you would choose an evaluation measure, detect overfitting and interpret a model result for learners at two experience levels.
Distributed data frameworks
The posting names Spark and Hadoop. Revise by sketching when distributed processing would be useful for a data-science task and explaining the roles of Spark and Hadoop without relying on a particular deployment.
Blended learning and education frameworks
The role supports blended learning and calls for familiarity with education or university frameworks and educational technology. Revise by outlining a lesson with an independent activity, a live explanation and a check of learner understanding.
Programme finances and risk
The role involves managing cohorts against time, budget and P&L requirements and analysing programme profitability. Revise by explaining which cost, revenue, schedule and risk measures you would review during a cohort.
Azure Machine Learning data and ML stack
Azure Machine Learning knowledge is a plus, not a requirement. As optional preparation, map the stages of a data-to-model workflow and practise explaining where an Azure ML stack could fit, without assuming a particular employer setup.

Skills to revise

SQL and data wrangling
High proficiency in SQL and data preparation, cleaning and wrangling is requested. Practise querying a messy dataset, resolving missing or duplicate records, joining tables and explaining your cleaning decisions.
Python modelling and visualisation
The posting names NumPy, Pandas, scikit-learn, Seaborn and Matplotlib. Practise a small workflow that prepares data, fits and evaluates a model, and plots results; rehearse how you would teach each step.
R programming
R is explicitly listed. Practise importing a dataset, producing a statistical summary and explaining the result in plain English suitable for a class.
BI reporting
Experience with tools such as Tableau and Power BI is listed. Practise building a concise dashboard from sample programme data that makes a profitability trend and its underlying measures clear.
Adaptive facilitation
The role requires facilitation or coaching experience, empathy and delivery adjusted to learner needs. Rehearse a short data-science explanation, then adapt it for a learner struggling with a prerequisite concept.
Cohort planning and monitoring
Extensive programme management experience and financial and risk management skills are requested. Practise drafting a cohort plan with milestones, budget checks and risks, then use sample figures to calculate and explain a variance.

Your preparation checklist

Map the stated requirements to evidence
Prepare a one-page evidence sheet for the required bachelor’s degree, facilitation or coaching experience, 5+ years in data science and machine learning, and programme management experience. Note which examples you can discuss without treating preferred certifications as required.
Build a data science project walkthrough
Prepare a concise walkthrough showing SQL and data cleaning or wrangling, a Python or R analysis, statistical reasoning, model evaluation, and clear visualisation. The posting names these capabilities; choose a project that genuinely demonstrates them.
Rehearse an adaptable teaching segment
Prepare a short English-language lesson on a data science concept, with a learner-needs check, an exercise and an alternative explanation for learners who need more support. This addresses the stated emphasis on facilitation, empathy and adjusted delivery.
Sketch a blended-learning plan
Draft a sample cohort plan showing how you would mentor learners, use educational technology and check skills outcomes across blended activities. Treat the plan as a discussion example, not as EduCLaaS’s prescribed delivery process.
Prepare a programme-management case
Outline an example of managing a programme against schedule, budget and risks, including a simple profitability analysis using cohort data. Be ready to explain how you would work within the learning delivery and operations policies named in the posting.
Review the optional Azure AI topics
If you have relevant experience, prepare an Azure Machine Learning example focused on its data and ML stack. The posting describes this knowledge as a plus, and Microsoft Power BI or Machine Learning certification as good to have—not requirements.
Write targeted questions for EduCLaaS
Ask how learner needs and skills outcomes are assessed, which educational technologies support blended delivery, and what information adjunct faculty receive to manage cohort budgets, P&L and risks.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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