Communicating Technical Skills and Expertise Questions
Focuses on how candidates describe their technical abilities, tools, and depth of expertise. Includes articulating which programming languages, frameworks, data tools or methodologies are known, describing the level of hands on experience, avoiding overstating competence, and describing contexts where the skills were applied. Interviewers use this to verify fit for role responsibilities and to probe for depth versus breadth.
MediumTechnical
0 practiced
A stakeholder asks for an immediate model-backed recommendation but you have limited time to run fairness and robustness checks. What do you tell them? Provide the exact words you would use and a short plan for mitigating risk while still delivering value quickly.
HardTechnical
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You must explain to legal why you used a particular third-party dataset for model training, including provenance, consent, and limitations. Draft a short justification (3-4 sentences) that balances technical necessity with compliance concerns.
MediumTechnical
0 practiced
Role-play: You have 10 minutes to convince a skeptical CTO that your choice of evaluation metric (e.g., AUC vs precision@k) is aligned to business goals. Prepare the script and two quick visual aids or data checks you would use to support your argument.
EasyBehavioral
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Describe your top three technical skills (languages, frameworks, or tools) that are most relevant to a data scientist role. For each skill, state: (1) years of hands-on experience, (2) a specific project where you applied it, (3) your self-rated proficiency (beginner/intermediate/advanced/expert), and (4) one concrete limitation you are actively working to improve. Be specific and avoid generic claims.
HardTechnical
0 practiced
A product team claims your model recommendations increased churn. You are asked to run a reproducible investigation. Draft an investigation plan describing data to collect, tests to run (A/B, causal methods), time window selection, and how you would communicate preliminary findings.
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