Technical practice
How would you use statistical analysis to decide whether an ML model is useful?
What to coverThe posting calls for strong statistical analysis and ML fundamentals. Suggested preparation: explain how you would choose evaluation measures, compare a model with a baseline, and check whether observed results are reliable.
When would you choose linear regression rather than logistic regression?
What to coverBoth algorithms are explicitly named in the posting. Suggested preparation: distinguish continuous-value prediction from classification, then discuss assumptions and how you would evaluate each model.
How would you diagnose and improve a decision-tree model?
What to coverDecision trees are among the algorithms listed. Suggested preparation: discuss overfitting, interpretability, and the trade-off between a tree's complexity and performance on unseen data.
How would you approach a clustering problem when there are no class labels?
What to coverThe posting specifies hands-on experience with clustering algorithms. Suggested preparation: explain how you would prepare features, assess whether groups are meaningful, and avoid treating clusters as proven business categories.
What checks would you make before training on messy data?
What to coverData cleaning, transformation, normalization, quality, and integrity are stated skills. Suggested preparation: cover missing or inconsistent values, transformations appropriate to the model, and safeguards against data leakage.
How would you investigate a model that performs well in development but poorly after deployment?
What to coverThe role includes implementing and optimizing ML models for production-ready systems. Suggested preparation: separate data-quality or distribution issues from model issues, and explain how you would test changes before adopting them.
How would you design an AI application pipeline for cloud or on-premises deployment?
What to coverThe role description explicitly mentions Cloud AI services, cloud or on-premises pipelines, and production-ready quality. Suggested preparation: describe the stages from input through inference to output, and discuss operational trade-offs without assuming a particular platform.
How would you decide whether a generative AI model belongs in a proposed solution?
What to coverApplying GenAI models is part of the stated role. Suggested preparation: compare the model's value with a simpler approach and explain how you would assess output quality and risks for the intended use.
What would change if a solution involved a chatbot or image processing?
What to coverThe posting lists chatbots and image processing as possible, not mandatory, work. Suggested preparation: choose one example and explain how its inputs, outputs, and evaluation would differ from a conventional tabular ML task.
How would you turn model results into a business insight?
What to coverThe role includes data analysis and interpretation for business insights, and names Tableau or Power BI. Suggested preparation: explain how you would select a clear visualization, communicate uncertainty, and distinguish an actionable finding from a correlation.
Behavioural practice
Tell me about a time you made a consequential ML team decision.
What to coverThe posting says this role is responsible for team decisions. Suggested preparation: use a real situation, describe the alternatives and your action, and explain the result and what you learned.
Describe a time you helped a team deliver difficult technical work.
What to coverThe role calls for collaborating with and managing a team to perform. Suggested preparation: give a situation, explain how you coordinated people or resolved a blocker, and state the outcome without overstating your individual contribution.
Tell me about a solution you developed with multiple teams.
What to coverThe posting expects engagement in key decisions and solutions that apply across teams. Suggested preparation: describe the shared problem, how you handled differing priorities, your actions, and the resulting decision or outcome.
Describe a time you led AI or ML research toward a practical decision.
What to coverLeading AI/ML research and development is an advertised responsibility. Suggested preparation: give a real example of the question investigated, how you assessed options, and what the evidence led your team to do.
Tell me about a time you explained a data finding to a business stakeholder.
What to coverThe role includes interpreting data to generate business insights. Suggested preparation: set out the situation, how you made the finding and its limitations understandable, and what action or decision resulted.