Decision Making Philosophy and Approach Questions
Describe your personal decision making framework and practical approach for tackling ambiguous or high stakes problems. Cover how you balance data and intuition, speed and rigor, short term wins and long term value, and how you involve others versus deciding autonomously. Explain the criteria you use to decide when to escalate, when to experiment, how you handle decisions with incomplete information, how you weigh trade offs, and how you communicate and operationalize decisions across teams.
EasyBehavioral
0 practiced
Explain the concrete criteria you use to decide whether to escalate a decision to leadership or make it autonomously as a data scientist. Include thresholds for impact (financial, customer, legal), time-to-decision constraints, cross-team scope, and an example of a decision you escalated and why.
EasyTechnical
0 practiced
List inexpensive, low-risk approaches you use to test core product assumptions before building a full ML solution. Give at least four examples (e.g., fake-door tests, Wizard-of-Oz manual interventions, rule-based prototypes, small pilots, retrospective analyses) and explain when each is appropriate.
HardTechnical
0 practiced
Quantify trade-offs between model complexity (e.g., deep ensembles) and maintainability/technical debt for long-term product stability. Propose metrics and thresholds to decide when complexity is justified, how to measure ongoing maintenance cost, and governance to revisit complex models periodically.
HardTechnical
0 practiced
Given continuous data drift and limited compute budget, propose an automated policy to decide when to refit a model, retrain from scratch, or incrementally update features. Include monitoring signals (performance decay, drift detectors), cost modeling, rollback strategies, and when to require human sign-off.
HardTechnical
0 practiced
A proposed product change offers significant short-term revenue but will likely increase a measurable demographic disparity. As data-science lead, prepare a recommendation for the executive committee: quantify impacts, propose mitigation options, outline legal/reputational risks, and recommend proceed/delay/redesign. Include how you'd monitor and enforce any mitigation.
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