Customer and User Obsession Questions

Demonstrating a deep commitment to understanding and advocating for customers and end users. Candidates should show how they prioritize user needs in decision making, even when it conflicts with other priorities, and provide concrete examples of advocating for users internally. Topics include using qualitative and quantitative research to surface user pain points, validating assumptions with user evidence, designing or improving experiences to solve real problems, maintaining ongoing connection to users through feedback loops, and influencing stakeholders to keep the organization user focused. Examples may range from entry level empathy and direct customer learning to strategic changes driven by user insight.

MediumTechnical

0 practiced

You have been asked to improve a recommendation model to reduce user churn. Describe how you would define and instrument target metrics, select signal features, design experiments, and balance short-term engagement improvements versus long-term retention objectives.

HardTechnical

0 practiced

You believe a proposed initiative will improve a short-term product metric but will erode long-term user trust due to opaque behavior. As an ML engineer, how would you present evidence to executive stakeholders, propose alternative approaches, and ensure decisions account for long-term user value?

MediumBehavioral

0 practiced

After launching a model-backed feature, explain specific practices you would use to maintain a connection to users and their workflows. Include monitoring dashboards, feedback rituals, how often you meet with support/product, and examples of actions you would take when you observe adverse user signals.

HardTechnical

0 practiced

Design an offline and online evaluation framework to detect metric inversion where an offline-improved model causes worse user engagement in production. Describe data to log, offline validation checks, shadow/phantom deployments, online experiment design, and automated alarms to catch metric inversion early.

MediumTechnical

0 practiced

Write a concise Python pseudocode outline for an audit script that computes per-cohort model performance from stored predictions and ground-truth labels. Cohorts are defined by country, age_bucket, and device_type. Describe how your script handles missing labels and low-sample cohorts, and how it flags cohorts for further investigation.

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