Managing Ambiguity, Assumptions, and Data Gaps Questions
Practice working with incomplete requirements, missing data, and ambiguous scenarios. Develop frameworks for identifying gaps, making reasonable assumptions, sanity-checking your assumptions against business logic, and adjusting assumptions when new information emerges. Learn to communicate assumptions clearly to stakeholders and discuss confidence in your modeling.
MediumTechnical
18 practiced
Marketing asks you to build a 'high value customer' segment, but nobody has defined what value means: revenue, margin, or purchase frequency. How do you land on a definition and make sure it holds up?
EasyTechnical
14 practiced
You're told to 'build a model to predict which customers will churn' and given nothing else. Walk me through what you actually do in the first hour before writing any code.
EasyTechnical
17 practiced
Before you start modeling on a dataset you've never seen, what quick checks do you run to catch problems like duplicated rows or features that leak information from after the label was determined?
EasyTechnical
21 practiced
You compute average order value for a segment and get $340, but a colleague says that seems way too high. Walk me through how you'd sanity-check that number before defending it.
EasyTechnical
28 practiced
You're building a fraud model and notice the 'merchant_category' feature is null for about 15% of transactions. How do you decide whether to drop it, impute it, or treat 'missing' as its own category?
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