Define the problem.
Then define the role.

Data engineering, analytics, and machine learning roles overlap, but they solve different problems. Start with the outcome and the system the person will own.

• Data engineering: pipelines, reliability, data models, and platform ownership.
• Analytics: business questions, measurement, reporting, and decision support.
• AI and machine learning: evaluation, model integration, deployment, and monitoring.

Candidates need to understand the maturity of the platform and the resources available. A research-heavy role differs from bringing an existing model into a product.

• Describe data availability, quality, access controls, and infrastructure.
• Clarify collaboration with engineering, product, and business teams.
• Define what success looks like and how it will be evaluated.

Ask candidates to explain decisions and results in projects relevant to the brief. Avoid requesting confidential datasets, source code, or customer information.

• Use a role-specific discussion or a bounded sample task.
• Evaluate how candidates identify uncertainty and measure results.
• Confirm the balance of hands-on delivery, strategy, and team leadership.