The base question felt manageable but the bias follow-up tripped me up a bit.
Use a specific example where data was central to a technical decision, walking through how you collected and analyzed the data, the trade-offs you considered, and the steps you took to challenge your own assumptions. Structure your answer with the STAR method, emphasizing the data-driven process and the outcome.
Pro tip: Quantify the impact of your decision with metrics (e.g., performance improvement, cost savings) and explicitly mention how you validated your assumptions, such as through A/B testing or peer review, to show you're not just data-driven but also self-aware.
Briefly describe the situation, the decision to be made, and why data was needed. Highlight the ambiguity or complexity involved.
Explain how you identified relevant data sources, collected the data, and performed analysis. Mention specific tools or methods (e.g., SQL, Python, dashboards).
Describe how you interpreted the data to derive insights, and discuss the trade-offs you considered (e.g., speed vs. accuracy, short-term vs. long-term impact).
Explain how you ensured your own biases or assumptions didn't skew the decision. This could include seeking diverse perspectives, running experiments, or validating with additional data.
State the decision you made based on the data, the actions taken, and the measurable results. Reflect on what you learned.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.