Algorithmic Thinking vs Interview Memorization

For years, engineers have been told that interview prep means grinding LeetCode and memorizing patterns. It’s tidy advice, but it's also deeply misleading. After watching thousands of people interview (and conducting a few thousand myself), one thing is obvious: the difference between candidates who sail through and those who stall out isn’t technical skill. It’s how they think during the interview. Most candidates treat interviews like exams. The best ones treat them like conversations about solving real problems. They don’t rush to code. They slow down. They question assumptions. They communicate trade-offs. They show the interviewer how they reason. The right way to prepare for interviews isn't memorizing tricks, but for rewiring how you approach technical conversations. Because once you stop prepping for the wrong thing, the right outcomes follow. Of course practicing problems helps, as does exposure to many different problems. But the devil is in the details. Are you memorizing as you practice, or are you learning to think? Finally, yes, sometimes you get a shitty interviewer who rewards memorization. That's a problem with our industry, but it's going away with the advent of cheating. We recently surveyed ~50 FAANG interviewers, and one of the clear trends is that today's interviewers are moving to testing for deep understanding rather than recall (link to that writeup in the comments).

I hired an engineer who couldn't invert a binary tree. Best hire I ever made. He hadn't done LeetCode in years. Said, "Honestly, I'd Google it." Most companies would've passed. I almost did too. So I gave him a real-world task. And he nailed it. He didn't guess: - He could debug like a detective. - He asked smart questions. - He explained his thinking clearly, even when under pressure. Six months in, he was leading projects. Making everyone around him better. Don't get me wrong: Algorithm questions are great - they test logical thinking under pressure. But they're just one lens. Big tech can afford to lose 20% of great candidates if they filter out 80% of poor ones. But when each hire is critical to your team's success, maybe we need a different filter. I’d rather hire someone who knows how to think, than someone who just remembers how to answer.

I've worked at Amazon, Oracle & Microsoft, and I can easily say that I've never been asked to reverse a linked list a day in my life. But what many forget is that there have been many instances where the learning I put into DSA has helped me out. With the chase of cracking DSA interviews and grinding LeetCode, we often forget that DSA is a fundamental CS subject, not just a box you check off. [1] You don't write DFS. But you think in DFS. When a massive component needs to be broken down, you're not sitting there coding a tree traversal. But your brain is doing the same thing, breaking a big problem into smaller pieces, going deep into one path, then backtracking and exploring another. That's DFS thinking. And it shows up everywhere. Debugging nested issues. Tracing a request through microservices. Refactoring a tangled module into clean parts. [2] You don't implement a queue. But you design with queues. Retry logic. Rate limiting. Load balancing. Task scheduling. These are all queue problems in disguise. If you've never thought about how a queue works under the hood, you won't naturally reach for it when designing a system. [3] You don't sort arrays by hand. But you think about trade-offs like a sorting problem. Should this be fast to write or fast to read? Should it optimize for the common case or the worst case? Do we need stability or speed? These are sorting trade-offs. [4] You don't use hashmaps to solve puzzles. But you use them to solve everything else. Caching. Deduplication. Lookups. Indexing. The number of problems that boil down to "use a hashmap" is honestly funny. But you only see that if you've trained your brain to recognize the pattern. [5] You don't do sliding window problems. But you think in windows. Monitoring error rates over the last 5 minutes. Calculating rolling averages. Rate limiting API calls. These are all sliding window problems. The pattern is the same, the context is different. LeetCode doesn't teach you to reverse linked lists for your job. It does teach you to recognize patterns. To break big problems into small ones. To think about edge cases before they become production incidents. The algorithms don't show up at work.  The thinking does. Every single day. So keep grinding. But don't grind just to pass interviews. Grind to build the depth of your understanding and expand your thinking. That's the real ROI of DSA.

How I Got 3 FAANG Offers (Without Grinding 500 LeetCode Problems) 👇 For years, I believed success in tech interviews was a numbers game. More LeetCode. More patterns. More problems. So I grinded. - Arrays. Trees. Graphs. - 50 problems. - 100 problems. Then burnout. Until I realized something simple: - I wasn’t failing interviews because I couldn’t solve hard problems. - I was failing because I didn’t think like a systems engineer. When I switched focus from memorization to understanding, everything changed. Instead of solving 500 LeetCode questions, I solved about 60 — but deeply. I stopped chasing new problems and started mastering thinking patterns. Here’s what I did instead 👇 💡 1. System Design Thinking I broke down how real systems work — load balancers, message queues, caching, databases. I studied case studies from Uber, Netflix, and AWS architecture blogs. 🧩 2. Real Projects > Fake Problems I built small-scale versions of production systems — a URL shortener, a chat app, a mini recommendation engine. This taught me more about scalability and tradeoffs than any “Two Sum” ever did. 🎙️ 3. Mock Interviews with Real Engineers I practiced with friends working at FAANG. Every session ended with one question: “Would you hire me?” Their feedback was brutal and priceless. 📘 4. Communication Practice I spent more time explaining why than how. Because interviewers aren’t testing syntax — they’re testing your ability to think out loud. Here’s the weekly routine that helped me land 3 offers: - Mon–Tue: Solve 2–3 LeetCode problems deeply (not quickly). - Wed: Study one real-world system architecture. - Thu: Build a small feature or side project applying what I learned. - Fri: Mock interview + review my thinking process. Weekend: Rest. Reflect. Write down 3 lessons from the week. No all-nighters. No burnout. Just consistency and clarity. FAANG interviews aren’t about who knows the most algorithms. They’re about who can reason, communicate, and design under pressure. -- If you’re serious about landing your dream offer — without losing your sanity — I teach this full system inside my FAANG Interview Accelerator, a private coaching program for high-performing engineers.

I don't use algorithmic exercises in SDET interviews. They only tell me whether someone prepared for algorithmic exercises. I felt the same as a candidate. Getting better at them often meant practising the same kind of problems over and over. They can show coding fundamentals. I just would not use them as the main signal for an SDET role. An algorithmic puzzle does not tell me enough about how someone would: 🔹 investigate a flaky test or a hard-to-reproduce bug; 🔹 identify risk in a feature change; 🔹 decide what to automate and what is not worth maintaining; 🔹 spot a test that passes without protecting meaningful behaviour; 🔹 make framework and tooling decisions; 🔹 think about test reliability, execution time, and feedback speed. I prefer exercises that look more like the work. A short feature description. A bug report. A failing test. An assertion with a coverage gap. A slow suite that fails intermittently. Then we can talk through it. What would they ask first? Where would they see risk? What would they test now, postpone, or investigate further? What would make a proposed test worth the cost of maintaining it? There is rarely one perfect answer, and I don't expect one. SDET work usually comes with incomplete context and trade-offs. I want to see how a person thinks when the answer is not obvious. That gives candidates room to show how they would work on the team. For the roles I hire for, that is the signal I need.

🚀 𝐂𝐫𝐚𝐜𝐤𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬 𝐈𝐬 𝐋𝐞𝐬𝐬 𝐀𝐛𝐨𝐮𝐭 𝐀𝐧𝐬𝐰𝐞𝐫𝐬, 𝐌𝐨𝐫𝐞 𝐀𝐛𝐨𝐮𝐭 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐂𝐥𝐞𝐚𝐫𝐥𝐲 Most people prepare for data science interviews by memorizing answers. Definitions. Formulas. Algorithms. This document highlights why that approach only works up to a point. Interviewers aren’t really testing whether you know what bias–variance tradeoff is or how k-means works. They’re testing whether you understand why these concepts exist and when to apply them. Take basic questions like supervised vs unsupervised learning or overfitting vs underfitting. On the surface, they look simple. In reality, they’re probing how you reason about data availability, problem framing, and generalization. As questions move from easy to hard, the shift is clear. It’s no longer about recalling definitions. It’s about tradeoffs. Why choose precision over recall? When does ROC-AUC fail? Why might a simple model outperform a complex one in production? The case-based questions are where this really shows. Churn prediction, A/B testing, fraud detection, recommender systems, each one forces you to think end to end. Data understanding, feature design, model choice, evaluation, deployment, and monitoring are all part of the same story. What I like about this material is that it blends theory with execution. You’re expected to explain concepts clearly, but also to reason practically under constraints like imbalance, noisy data, changing patterns, and real business impact. That’s what good data science interviews look for. Strong candidates don’t rush to models. They start with assumptions, risks, and metrics. I’m uploading this document because it’s a solid way to practice that mindset. Whether you’re preparing for interviews or trying to sharpen your fundamentals, working through questions like these builds clarity that transfers directly to real-world projects. Interviews reward clarity of thought far more than memorized answers. #DataScience #DataScienceInterviews #MachineLearning #AI #Analytics #Statistics #MLFundamentals #TechCareers #InterviewPrep #LearningInPublic #BuildInPublic

Today, I witnessed my team lead interviewing a Full-Stack Web Developer with 2 years of experience, and I observed some interesting insights. The interview started with basic technical questions, which the candidate answered well. Then, my team lead gave him a coding task: 👉 "Write a function that removes duplicate values from a nested array while preserving its structure (PHP)." The candidate struggled and couldn’t solve it. So, my lead gave him another task: 👉 "Write a function that groups an array of associative arrays based on a specific key." Again, he couldn’t solve it. At this point, he admitted: "I don’t always have syntax and logic rules memorized, as I usually rely on resources like Stack Overflow and ChatGPT. Could you assign me a real-world task instead?" Here,I decided to step in. Since I was working on WooCommerce project, I gave him this challenge: 💡 "Implement a functionality where a logged-in user gets a 15% discount if they place two orders in a single day." We allowed him to use Google, ChatGPT, or any other resources. To our surprise, within 1.5 hour, he understood WooCommerce functionality, analyzed the existing code, and successfully implemented the feature. ✨ We ended up hiring him! ✨ Key Takeaway Many traditional interview techniques overemphasize logical puzzles and syntax-heavy questions, yet modern developers approach problem-solving differently. Rather than memorizing solutions, they adapt, learn, and implement on the spot using the tools and resources available to them. 💡 Instead of testing how well candidates remember syntax, we should focus on how effectively they apply problem-solving skills to practical challenges. What’s your take on this? Let’s discuss in the comments! 👇 #HiringTechTalent #TechCommunity #WhatDoYouThink

In my latest blog post, I explore the challenge of distinguishing between candidates who rely on memorized solutions from platforms like LeetCode and those who genuinely understand coding. As a technical screener, I find the phone screen to be a crucial step in the interview process. It offers insights into a candidate's abilities before moving to the on-site interview stage. The issue arises when candidates use these platforms to memorize algorithms, sometimes even cheating during remote interviews. To address this, I've developed a strategy that involves modifying popular questions to make them simpler. This approach helps identify true coding skills, as genuine coders adapt to the changes, while "grinders" struggle. This method has improved my team's quality and increased my on-site pass rate, proving effective in filtering candidates with real coding proficiency. https://lnkd.in/eSSrpkKz

Hiring for data roles is tricky. Some companies focus on SQL and business case questions, while others emphasize data structures and algorithms (DSA). I lean toward DSA-based interviews, and here’s why. SQL is important, no doubt. But SQL is a tool; a skill that can be learned quickly. It doesn’t tell me how someone thinks. DSA, on the other hand, reveals problem-solving ability, adaptability, and structured thinking. Here’s what I’ve seen: ✅ Strong DSA skills correlate with better long-term problem-solving. ✅ DSA tests a candidate’s ability to break down complexity, a must-have for real-world data challenges. ✅ SQL-heavy interviews often favor memorization over reasoning. If someone can think through recursion, dynamic programming, or graph traversal, they can certainly learn SQL. But the reverse isn’t always true. Of course, the best interviews mix both. But if I have to pick one? I’ll take someone who can build the right algorithm over someone who just writes efficient queries. That said, I don’t use DSA interviews just to find the “optimal” solution like big tech does. I use them to evaluate problem-solving, thinking, communication, and collaboration. I often don’t even care if the solution is optimal, or even completed. It’s about how you approach the problem, not just whether you get the right answer. What do you think? Are we overvaluing or undervaluing DSA in data hiring? #GetHired2025 #Algorithms #DSA #SQL #Python #DataScience

A friend who interviews engineers told me about a candidate. The guy drew a near-perfect social feed in ten minutes. Caching, fan-out, all of it. Clearly studied hard. Then my friend changed one thing: "Half your users are on 3G and your cache goes cold every few hours. Now what?" He froze. Probably not because he didn't know — he knew more than most. The problem was that he'd memorized an answer, but not the reasoning under it. That's what trips people up with system design prep. You can grind it like LeetCode, expecting confidence to show up. But design has no clean pass/fail. Two good engineers pick different trade-offs and both are right. It isn't a knowledge problem. It's a judgment problem. And you can't memorize judgment — you build it by sitting in the ambiguity until things make sense. I wrote about how you can practice system design interviews to ensure you don't freeze if things go off track: https://lnkd.in/dE82sA5K #systemdesign #softwareengineering #techinterviews #coding