Technical practice
How would you use Python, exploratory analysis and inferential statistics to assess a proposed business insight?
What to coverThe posting calls for expert-level understanding of all three. Suggested preparation: discuss data-quality checks, relevant comparisons, uncertainty and how you would avoid overstating a finding.
How would you test and integrate a new external data source for enterprise analytics?
What to coverThe role includes identifying new information sources and curating external datasets, with data drawn from sources such as BigQuery, flat files and cloud storage. Suggested preparation: explain how you would assess relevance, accuracy, join keys and repeatable access using SQL where appropriate.
How would you choose between binning, aggregation, pivoting and encoding for a model?
What to coverThe posting explicitly names these feature-engineering techniques and calls for business acumen in applying them. Suggested preparation: explain what each transformation preserves or loses, and how you would check for leakage and validate its effect on model performance.
How would you build and evaluate a supervised model for text classification?
What to coverSupervised machine learning is required, and text classification is a listed use case. Suggested preparation: describe label quality, a baseline, suitable evaluation measures and how you would inspect important error types.
How would you evaluate a demand-forecasting model before recommending its use?
What to coverDemand forecasting is a listed use case, and forecasting is one of several named specialist areas. Suggested preparation: discuss time-aware validation, a relevant baseline, forecast-error measures and how performance might vary across business segments.
How would you assess whether a market mix model supports a business recommendation?
What to coverMarket mix modelling is a listed use case and one of the named specialist areas. Suggested preparation: explain assumptions about the inputs, uncertainty in estimated effects and the limits of drawing causal conclusions from observational data.
How would you formulate and assess a logistics or price-optimization problem?
What to coverThe posting lists logistics and price optimization as use cases and names linear and network optimization among its specialist areas. Suggested preparation: define an objective, business constraints and a baseline, then explain trade-offs and checks that a proposed solution is usable.
What would make a machine-learning model repeatable, dynamic and scalable?
What to coverThe role calls for models with those properties and for use of cloud-based data sources. Suggested preparation: discuss reproducible data transformations, changing inputs, testing, monitoring and the trade-off between automation and operational complexity without assuming a required platform.
How would you troubleshoot a model or automation that fails when handed to a technical team?
What to coverThe posting calls for collaboration with development and infrastructure teams and troubleshooting niche technologies. Suggested preparation: describe how you would isolate data, code and environment causes, communicate evidence, and verify a fix.
How would you evaluate an agentic accelerator for data-science productivity?
What to coverThe role includes partnering on agentic accelerators, while knowledge and use of agentic or generative tools is preferred. Suggested preparation: propose a limited task, a measurable productivity comparison and checks for output accuracy and human review.
Behavioural practice
Tell me about a time you translated a business partner’s objective into an analysis.
What to coverHelping business partners meet objectives through analysis and modelling is a stated responsibility. Suggested preparation: use situation, action and result to show how you clarified the decision, chose an approach and determined whether the work helped.
Describe a time you explained a data-driven recommendation to people outside data science.
What to coverThe posting emphasizes clear stories that explain the why and how to cross-functional teams. Suggested preparation: use situation, action and result to show how you adapted the explanation, handled uncertainty and learned whether it was understood.
Tell me about a time feedback from another data scientist changed your approach.
What to coverThe role calls for asking questions, engaging and soliciting feedback from other data scientists. Suggested preparation: use situation, action and result to explain the feedback, your response and its effect on the work.
Describe a time you collaborated with development or infrastructure colleagues to resolve a technical obstacle.
What to coverCross-team technical collaboration and troubleshooting are stated duties. Suggested preparation: use situation, action and result to distinguish your contribution, how you coordinated the investigation and what was resolved.
Tell me about a time you improved a team’s way of delivering data-science work.
What to coverThe posting mentions Agile sprints and identifying long-term data-science processes, frameworks, tools and standards. Suggested preparation: use situation, action and result to describe a specific change, how you gained input and what evidence showed whether it helped.