🔴 1. Not Understanding the Business Problem
Mistake: Jumping straight into modeling without clarifying what the problem is.
Fix: Ask clarifying questions and restate the objective in your own words before proposing solutions.
🔴 2. Overemphasizing Algorithms Over Problem Solving
Mistake: Talking too much about complex ML models and not enough about data understanding or impact.
Fix: Focus on solving the right problem efficiently, not using the fanciest model.
🔴 3. Poor Communication
Mistake: Using too much technical jargon or not being able to explain your work simply.
Fix: Practice explaining models and results to a non-technical audience.
🔴 4. Ignoring EDA and Data Cleaning
Mistake: Jumping into modeling without talking about data exploration and preprocessing.
Fix: Always highlight your steps for data quality checks, missing value handling, outlier detection, etc.
🔴 5. Not Knowing Basic Statistics
Mistake: Struggling to explain p-values, confidence intervals, or distributions.
Fix: Brush up on foundational statistics—many questions are based on these.
🔴 6. Overfitting on Resume Projects
Mistake: Failing to deeply explain projects listed on your resume.
Fix: Be ready to discuss the objective, dataset, features, algorithms, and outcomes of your projects.
🔴 7. Poor SQL or Python Coding
Mistake: Making syntax errors or writing inefficient queries.
Fix: Practice hands-on coding problems regularly on platforms like Leetcode, Hackerrank, or StrataScratch.
🔴 8. Misunderstanding Evaluation Metrics
Mistake: Choosing the wrong metric (e.g., accuracy on imbalanced datasets).
Fix: Understand when to use precision, recall, F1-score, ROC-AUC, etc.
🔴 9. Inadequate Preparation for Behavioral Questions
Mistake: Being unprepared for questions like "Tell me about yourself" or "Describe a challenge."
Fix: Use the STAR method (Situation, Task, Action, Result) to structure your answers.
🔴 10. Focusing Only on Tools, Not Concepts
Mistake: Relying on libraries like scikit-learn or pandas without understanding the underlying algorithms.
Fix: Study how models work internally (e.g., how decision trees split, what gradient descent does).
🔴 11. Not Asking Questions at the End
Mistake: Saying “No questions” when asked at the end of the interview.
Fix: Prepare thoughtful questions about the team, projects, or company challenges.
🔴 12. Not Practicing Case Studies
Mistake: Being caught off-guard by business case questions like churn prediction or A/B testing.
Fix: Practice framing and solving open-ended case problems.
🔴 13. Poor Time Management in Coding Rounds
Mistake: Spending too much time optimizing instead of finishing the solution.
Fix: Get a working version first, then optimize if time allows.
🔴 14. Being Too Rigid With Answers
Mistake: Sticking to one way of solving a problem without considering alternatives.
Fix: Be flexible and open to suggestions; show you're collaborative.
🔴 15. Not Following Up
Mistake: Not sending a follow-up thank-you email.
Fix: Send a concise, polite message thanking the interviewer and reiterating your interest.
✅ Pro Tip:
Mock interviews and recording yourself explaining your projects can uncover blind spots and dramatically improve your confidence.
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