Faculty.ai Data Scientist Interview Questions
The questions to prepare for a Faculty.ai Data Scientist interview. Questions from real interview reports rank first. Updated weekly.
Outline the first checks to diagnose a sudden drop in a core product metric, starting with data quality, scope, and decomposition.
Diagnose a post-release KPI drop by separating instrumentation issues from real behavior changes and tracing the problem through the metric hierarchy.
Describe a machine learning project, from problem framing and feature work to model training and evaluation.
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Use a two-proportion z-test and power analysis to explain p-value and statistical power for an onboarding A/B test.
Explain how NULL values affect JOIN results, when LEFT JOIN is safer, and how to handle NULLs correctly in PostgreSQL.
Analyze weekly service delivery performance by team using joins, a CTE, date aggregation, and KPI calculations.
Deloitte
ReputationUse joins, CTEs, and aggregations to find the weakest step in an onboarding funnel and estimate lost conversions.
Atlassian
RevolutUse a CTE and window functions to calculate daily, cumulative, and three-day moving transaction volumes by Mastercard MCC.
MastercardIdentify the main pitfalls that can distort A/B test interpretation and explain how to guard against them.
Explain which classification metrics to use and how metric choice depends on the business objective and error tradeoffs.
Framework for choosing a feature's primary success metric and guardrails before launch.