73. What is A/B testing and how do you design one?
74. What is a control group and treatment group?
75. What is statistical significance in A/B tests?
76. What is confidence interval for conversion rate?
77. What is uplift modeling?
78. What is feature importance and how do you interpret it?
79. How do you explain a model’s prediction to a non‑technical stakeholder?
80. How do you monitor a deployed model in production?

🧠 Behavioral & Case‑Study Questions

81. Walk me through a data science project you led from end‑to‑end.
82. Tell me about a time you improved a metric using data science.
83. Tell me about a time a model failed and how you fixed it.
84. Tell me about a time you explained technical results to non‑tech stakeholders.
85. Describe how you would build a churn‑prediction model.
86. Describe how you would build a recommendation system.
87. Tell me about a time you worked with messy or incomplete data.
88. How do you prioritize data‑science initiatives?
89. How do you handle conflicting requirements from business and data teams?
90. How do you stay up to date with data‑science trends and tools?

🚀 Advanced & Specialized Topics

91. What is time‑series analysis and forecasting?
92. What is ARIMA / SARIMA / Prophet?
93. What is deep learning for data science?
94. What is neural network basics and backpropagation?
95. What is NLP for data science (e.g., sentiment analysis)?
96. What is computer‑vision basics for a data scientist?
97. What is causal inference and counterfactuals?
98. What is explainable AI (XAI) and why is it important?
99. How do you balance interpretability vs performance?
100. What skills do you think are most important for a modern data scientist?

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