Problem Definition and Framing Questions

Covers the skills and practices used to clarify, diagnose, and scope ambiguous business or product problems into actionable problem statements before proposing solutions. Candidates should demonstrate structured and insightful clarifying questions to understand business context, current and desired states, target users and user needs, success metrics and desired outcomes, constraints such as budget, timeline, technical dependencies, and compliance, stakeholder perspectives, and existing performance baselines. Includes separating symptoms from root causes, surfacing and testing hypotheses, identifying data to collect and analyze, performing root cause analysis, breaking complex problems into prioritized subproblems, and defining acceptance criteria and next steps or experiments to reduce uncertainty. Encompasses discovery techniques and basic user research to surface user pain points and opportunities, requirements scoping including scope boundaries, risks and trade offs, and the ability to write a concise problem statement in your own words. At senior levels also assess strategic framing, avoiding premature solutions, aligning stakeholders, and presenting an executive narrative that links diagnosis to measurable outcomes and implementation trade offs; for junior candidates emphasize curiosity, systematic thinking, and the ability to prioritize information needs rather than jumping to implementation.

HardTechnical

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A regulator requires explanations for automated credit decisions. As the ML engineer responsible for problem framing, propose a strategy that balances predictive performance with explainability. Include stakeholders, acceptance criteria for explanations, options for interpretable models or explanation techniques (e.g., rule lists, SHAP, surrogate models), and operational fallbacks (manual review) for edge cases.

HardTechnical

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Product wants ML to detect emerging abuse types that have little or no historical labels (zero-shot). Frame the problem: list discovery techniques to surface previously unseen behavior, data collection (human-in-loop, synthetic examples), evaluation strategies for rare/novel classes, and an experimental roadmap to reduce uncertainty and prioritize labeling.

HardSystem Design

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Design an experimental approach that convincingly demonstrates that an ML-based recommendation engine increases customers' lifetime value (LTV) compared to a heuristic baseline. Include primary and auxiliary metrics, incremental attribution method, treatment assignment strategy, necessary power calculations for long-horizon LTV, and guardrails to prevent negative short-term outcomes.

MediumTechnical

0 practiced

You must define acceptance criteria and ethical guardrails for a behavior-change ML feature that nudges users toward healthier habits. Explain what metrics, consent mechanisms, subgroup analyses, and rollout checks you would include to minimize unintended harms while measuring success.

MediumTechnical

0 practiced

You see a sudden increase in false positives from a classifier in production. Describe how you would surface and test multiple competing hypotheses (data drift, upstream changes, label definition shift, adversarial activity). Explain how you would prioritize hypotheses and what quick experiments or analyses you'd run to eliminate or confirm each.

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