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Walk through a DS project end-to-end

OneMain FinancialData Scientist · Technical Screen
EasyData ScientistTechnical ScreenBehavioral & Leadership

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This question evaluates end-to-end data science competencies including problem framing, metric design, data sourcing and quality assessment, modeling and evaluation choices, risk identification, impact quantification, and stakeholder communication within the Behavioral & Leadership category for a Data Scientist position.

Prompt

Describe one data science / analytics project you worked on, end-to-end.

What to cover

Include concise but concrete details on:

  • Problem & goal: What business/user problem were you solving? Who were the stakeholders?
  • Success metrics: What was the primary metric? What diagnostic and guardrail metrics did you track?
  • Data: What data sources did you use? Key tables/events, main features, and major data quality issues.
  • Method: What analysis/modeling approach did you choose and why (alternatives considered)?
  • Evaluation: How did you validate results (offline metrics, backtests, A/B test, quasi-experiment)?
  • Risks & assumptions: Confounders, leakage risk, selection bias, missing data, seasonality.
  • Outcome & impact: What changed as a result? Quantify impact if possible.
  • Iteration: What would you improve with more time?

Follow-ups (interviewer may ask)

  • What was the hardest tradeoff you made?
  • What did you do when results contradicted expectations?
  • How did you communicate uncertainty and limitations?