EvenUp·Data Scientist·Take-home Assignment
May 2026
Take-home modeling task for a Data Scientist role at EvenUp. The assignment was a classic wine quality prediction problem on a clean CSV, covering EDA, feature selection, model building, and feature importance. Pretty well-scoped for a take-home, nothing too surprising.
- You're given a red wine dataset. What do you learn from exploring it? Walk through at least three concrete findings from distributions, outliers, correlations, or target imbalance, and explain how each one shapes your modeling decisions.
- Before fitting any model, which features do you think will be most predictive of wine quality, and what are two different ways you'd assess that?
- Build a model to predict wine quality. Justify whether you frame it as regression, classification, or ordinal classification, describe your validation strategy, and state your evaluation metric.
- After fitting your final model, how do you determine which features are actually driving predictions? What method fits your model choice, and what pitfalls should you watch out for?
“This is the part I actually enjoy but also the part where I tend to over-explain.” The rest of the author's notes on Data Scientist interview at EvenUp, Take-home Assignment round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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