Data Scientist Screening Interview Template

Describe a machine learning model you built that was deployed in production. What problem did it solve and what was the measurable business impact?

What this assesses: Evaluates end-to-end ML experience and ability to connect technical work to business outcomes.

Walk me through how you would approach a prediction problem where you have limited labeled data. What techniques and strategies would you consider?

What this assesses: Tests depth of ML knowledge including transfer learning, semi-supervised methods, and data augmentation strategies.

Tell me about a time your model performed well in testing but poorly in production. What went wrong and how did you diagnose and fix the issue?

What this assesses: Assesses understanding of model deployment challenges, data drift, and debugging methodology.

How do you decide between a simple statistical model and a complex machine learning approach for a given problem?

What this assesses: Evaluates pragmatic thinking, understanding of bias-variance trade-offs, and resistance to over-engineering.

Describe your experience with data engineering and data pipelines. How do you ensure data quality and reliability for your models?

What this assesses: Tests awareness of data infrastructure and quality issues that affect model performance.

How do you communicate model results and uncertainty to non-technical stakeholders? Give a specific example.

What this assesses: Assesses communication skills and ability to make technical concepts accessible.

Tell me about an A/B test you designed. How did you determine sample size, handle multiple comparisons, and interpret the results?

What this assesses: Evaluates experimental design knowledge and statistical rigor.

What areas of data science are you most interested in developing further, and why does this role appeal to you?

What this assesses: Gauges growth mindset, specialization interests, and alignment with the team's focus areas.