Technical practice
How would you use SQL to prepare data for a predictive analytics project?
What to coverSuggested preparation: Explain joins, aggregation, and checks for missing or duplicated records. Describe how you would confirm that the resulting data matches the question being analyzed.
How would you use Python, Pandas, and NumPy to investigate a dataset?
What to coverSuggested preparation: Walk through inspecting types, distributions, missing values, and outliers before transforming data. Explain how you would check that transformations preserve the meaning of the data.
How would you define a predictive modeling problem from a business objective?
What to coverSuggested preparation: Describe how you would choose a target, identify usable inputs, and establish when a prediction must be available. Address the risk of using information that would not exist at prediction time.
How would you evaluate whether a predictive model is useful?
What to coverSuggested preparation: Discuss a baseline, an appropriate validation split, and metrics tied to the decision the model supports. Explain how you would compare performance with the complexity and limitations of the model.
When might you choose Scikit-learn rather than TensorFlow for a machine-learning task?
What to coverSuggested preparation: Compare the tools in terms of problem type, data volume, model complexity, and ease of experimentation. Avoid assuming either tool is required for every project; the posting lists them as alternatives.
What would you investigate if a model performed well during development but poorly on new data?
What to coverSuggested preparation: Consider leakage, overfitting, differences between training and new data, and changes in data quality. Explain how you would isolate the cause before revising the model.
How would you design a business-intelligence dashboard in Power BI or Tableau?
What to coverSuggested preparation: Start with the audience's decisions and define measures consistently before selecting views. Discuss how you would check calculations, make filters understandable, and avoid a dashboard that obscures the main findings.
How would you visualize an analytical finding without overstating it?
What to coverSuggested preparation: Match the chart to the comparison or trend and use clear labels and scales. Explain how you would communicate uncertainty or data limitations alongside the finding.
How would you assess whether an analytics solution supports a business objective?
What to coverSuggested preparation: Distinguish a model or dashboard's technical performance from its usefulness for a decision. Suggest a measurable outcome and describe what evidence would be needed to assess its effect; the posting does not specify particular business projects.
Behavioural practice
Tell me about a time you turned an unclear business question into an analytics task.
What to coverSuggested preparation: Use a real situation, the questions you asked, the scope you agreed on, and the result. If relevant, explain how you handled a mismatch between the requested analysis and the available data.
Describe a time you explained a complex analytical finding to a nontechnical audience.
What to coverSuggested preparation: Give the situation, how you adapted your explanation or visualization, and the result. Make clear what the audience needed to decide without claiming a business impact you cannot substantiate.
Tell me about a time an analysis or model did not work as expected.
What to coverSuggested preparation: Identify the problem, the checks and changes you made, and the outcome. Explain what the experience taught you about data quality, evaluation, or communicating limitations.
Describe a time you coordinated analytical work with others while working remotely.
What to coverSuggested preparation: The posting describes a remote-first setup, so this is a suggested practice topic, not a stated interview question. Use a real situation, your actions to clarify work and share findings, and the result.