Product Sense and Intuition Questions

Ability to understand users, markets, and product tradeoffs and to form well grounded product judgments. This includes identifying user needs, pain points, and behavior patterns through qualitative and quantitative research; applying frameworks such as Jobs to Be Done, user journey mapping, and hypothesis driven discovery; diagnosing friction in experiences and proposing concrete improvements that balance simplicity, usability, and feature richness. It also covers product instincts and critical thinking about product design, business models, metrics, growth levers, and market trends. Candidates should be able to explain why a product works or fails, articulate favorite products and specific changes they would make, prioritize features with clear rationale and expected impact, and communicate how their suggestions would be measured and validated.

EasyTechnical

0 practiced

Using the Jobs-to-Be-Done framework, describe the primary job a personalized news feed is hired to do for users. List three user pains, three measurable product outcomes (KPIs), and propose one ML-driven hypothesis to address a key pain. Explain how you'd validate the hypothesis with qualitative and quantitative methods.

MediumTechnical

0 practiced

You inherit a recommender system with stale models and poor KPIs. As an ML engineer, propose a 12-month roadmap that includes quick wins, foundational data and infra work, experimentation cadence, milestones, success metrics, dependencies, and risks. Prioritize items that balance impact and delivery speed.

EasyTechnical

0 practiced

Describe the cold-start problem in personalization systems from a product perspective. Provide three engineering and three product-level strategies (short-term and long-term) you'd propose to improve the new-user experience and explain which measurable signals you would use to demonstrate improvement.

MediumTechnical

0 practiced

Explain how you would use cohort analysis to detect model performance regressions that affect only certain user segments (for example, premium users or users from a particular acquisition channel). Describe which cohorts to define, which metrics to monitor per cohort, alerting strategy, and next steps for investigation.

HardTechnical

0 practiced

Propose three product experiments to test whether adding explainability features (e.g., 'why this was recommended', confidence scores, counterfactual suggestions) increases user trust and engagement in a content discovery product. For each experiment define the hypothesis, primary and secondary metrics, instrumentation plan, and success criteria.

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