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Why I rejected “strong” Data Scientists for this role 💻🛡️ When I was hiring for Senior Data Scientist roles, I noticed the same pattern again and again in interviews. Many candidates had solid tec
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This SQL question shows up in almost every interview. Finding duplicate records is not about memorizing queries. It’s about explaining how you think. In interviews, what matters is: • Understanding
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A strong ML project is not enough. Most candidates get rejected because they explain *what they built* instead of *why it mattered*. In 2026, interviewers care about: • Business context • Trade-off
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https://jobs.akamai.com/en/sites/CX_1/job/2594/?utm_medium=jobshare&utm_source=External+Job+Share. #datascience #datascientist #datasciencejobs #bangalorejobs #hiring
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Most candidates fail this interview question because they explain definitions instead of showing judgment. Train/Test Split and Cross-Validation aren’t competing tools — they solve different problems.
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Normalization vs Standardization: When to use what 🎯 ━━━━━━━━━━━━━━━━━━━━ NORMALIZATION (0-1 scaling): Use when: → Neural networks (bounded inputs better) → NO outliers in data → Need specific bou
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Most candidates explain formulas. Strong candidates explain trade-offs. Precision = How many predicted positives were correct Recall = How many real positives did we catch The right metric depends
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Salary Negotiation: What hiring managers expect but won’t say 🎯 After 20+ offers, here’s the truth we don’t tell candidates: We EXPECT you to negotiate. We budget 15-20% above the first offer. Not
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📌 DS Interview Classic (that filters senior candidates) Q: Why does R² always increase when you add more features? Because R² only measures fit on training data. Adding features can never increase
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This DS interview question sounds simple — but it isn’t. In interviews, most candidates jump straight to: ➡️ “The model has good accuracy” That’s not the answer interviewers are listening for. What
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MAE vs MSE — the answer interviewers actually want 👇 Most candidates explain formulas. Strong candidates explain behavior. Intuition: • Most prediction errors are small • One outlier (bad data / r
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Most candidates know linear regression. Very few can explain it correctly in interviews. Here’s the common mistake I see 👇 People think linear regression is about a straight line. Interviewers care
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About @datascience.interview

datascience.interview is a Instagram nano influencer. With 5.2K followers and 37 posts, datascience.interview's account shows a follower-to-following ratio of N/A:1. View all content anonymously on Pictame - no login required.

datascience.interview's engagement rate sits at 7.24%. That's 45% above what you'd typically see for accounts with 1K-10K followers (average 5%) — a sign of a genuinely connected audience.

Out of the last 12 posts, 100% are videos and 0% are photos. Content shows up about every 7 days. Captions lean detailed, averaging 796 characters.

7.24% engagement rate — 45% above average for 1K-10K follower accounts.
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Data Science Manager @ Akamai | Boston 🇺🇸 Real DS & ML Interview Questions Daily SQL • ML • Stats ⬇️ Free Interview Gu...

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