Bilwasiva is a senior AI Engineer at Amazon. We gave him one real interview question and let him walk through his entire thought process out loud, uncut, for 20 minutes. No cuts. No retakes. Just how a working AI Engineer actually thinks through a problem the way they would in a real interview room. If you're a software engineer or data scientist trying to break into AI engineering in 2026, this is the closest you'll get to sitting in on a real interview at a top company. Start nailing your tech interviews: https://www.interviewquery.com/signup?utm_source=youtube&utm_medium=video&utm_campaign=ai_engineer_walkthrough Use code "datasciencejay" for 10% off your tech interview prep: https://www.interviewquery.com/pricing?via=jay In this video, you'll learn: • How a senior AI Engineer breaks down an ambiguous question • Where most candidates get stuck and how Bilwasiva avoids those traps • The mental model he uses to structure a 20-minute answer • What interviewers are actually listening for in AI Engineer interviews • How to communicate technical decisions out loud under pressure Learn more about being an AI Engineer :robot_face: Check out AI project ideas: https://www.interviewquery.com/p/ai-project-ideas?utm_source=youtube&utm_medium=video&utm_campaign=ai_engineer_walkthrough :question: Practice top AI Engineer interview questions: https://www.interviewquery.com/questions?utm_source=youtube&utm_medium=video&utm_campaign=ai_engineer_walkthrough :books: Explore company interview guides: https://www.interviewquery.com/companies?utm_source=youtube&utm_medium=video&utm_campaign=ai_engineer_walkthrough Connect with Jay :memo: Read Jay's personal blog: https://datastream.substack.com/ :briefcase: Follow Jay on LinkedIn: https://www.linkedin.com/in/jay-feng-ab66b049/ Connect with Bilwasiva :briefcase: Website: https://www.mentoring-club.com/profiles/bilwasiva-basu-mallick#mentor-profile #AIEngineer #AIEngineerInterview #MockInterview #TechInterviews #InterviewQuery #SoftwareEngineering #MachineLearning #LLM #PromptEngineering #TechCareers #AIcareers

You start talking about fraud detection: models, signals, features. And then the interviewer asks: “What’s the net consequence of fraudulent usage?” Full mock interview here: https://youtu.be/hIMxZyWw6Ug It sounds simple. But this is where most candidates pause. Because this question forces you to step back and define what actually matters, before jumping into solutions. Without that, even a technically correct answer can fall apart in an interview. This clip shows a mock interview moment where the candidate is pushed to step back and define the impact of fraud clearly before jumping into solutions. That’s where most people struggle in data science and machine learning interviews, not the algorithms, but understanding what actually matters. If you’re preparing for data science, analytics, or AI/ML interviews, this is exactly the kind of question that can make or break your performance. Full mock interview here: https://youtu.be/hIMxZyWw6Ug #frauddetection #datascienceinterview #caseinterview #datascienceforbeginners #interviewtips #datascientist #interviewpreparation

Most candidates can solve this software engineering interview question. They still fail the interview. 3Sum Closest looks simple, until you’re asked to optimize it and explain your thinking under pressure. The question we're tackling: https://www.interviewquery.com/questions/targeted-sum?utm_source=youtube&utm_medium=social Use the code "datasciencejay" and get 10% off data science interview prep :fire:: https://www.interviewquery.com/pricing?via=jay In this video, Vivek, a senior engineer from Microsoft (previously at Google, Walmart, and Amazon), explains how to approach this problem the right way, from brute force to optimization, while thinking out loud like you would in an actual interview. Most people get this wrong because they jump straight into coding, rely on memorized solutions, and never practice explaining their thought process. Strong candidates, on the other hand, start with a simple approach, improve it step by step, and think out loud as they work through the problem. That’s exactly what this video demonstrates. This is not a polished “here’s the final answer” tutorial. You’ll see: 1. How to break down the problem from scratch. 2. How to move from brute force to optimized solution 3. How to think out loud while coding 4. How to debug when things go wrong 5. How to handle follow-up questions What you’ll learn 1. How to approach array problems like 3Sum 2. How to structure your thinking in interviews 3. How to optimize step by step 4. How to communicate your solution clearly 5. How to handle mistakes and recover TIMELINE 00:00 Problem introduction (3Sum Closest) 00:34 Interview context (experience at top companies Google, Amazon, Walmart, Microsoft, etc.) 01:43 Understanding the problem with an example 02:43 Brute force approach (3 loops) 04:54 Time complexity discussion 06:02 Coding brute force solution 07:59 Optimized approach (sorting + binary search) 11:03 Binary search deep dive 16:21 Debugging + fixing edge cases 17:50 Wrap-up + interview advice 18:11 Preparing for Your Next Round of Interviews #leetcode #softwareengineer #coding #interviewtips #3sumclosest

Most candidates think fraud detection is about models and features. Then they get asked: “What is a fraudulent user?”…and freeze. Watch the full breakdown and learn how to approach these questions step by step: https://youtu.be/hIMxZyWw6Ug This video shows a mock interview moment where the candidate is pushed to step back and clearly define the problem before jumping into solutions. That’s where most people struggle in data science and machine learning interviews. It's not the algorithms, but structuring their thinking under pressure. If you’re preparing for data science, analytics, or AI/ML interviews, this is exactly the kind of question that can make or break your performance. Full mock interview here: https://youtu.be/hIMxZyWw6Ug #frauddetection #datascienceinterview #caseinterview #datascienceforbeginners #interviewtips #datascientist #dataengineering #interviewprep

Most people completely freeze when interviewers ask: “What did you do wrong?” It’s not really about the mistake. It’s about whether you can evaluate yourself under pressure. Watch full video mock video: https://youtu.be/hIMxZyWw6Ug This clip shows how one candidate handled it and what interviewers are actually looking for. In this mock interview clip, the candidate breaks down what they think went well, what didn’t, and what they’d improve in real time. That kind of self-awareness is exactly what interviewers look for (and most candidates miss). Full mock interview here: https://youtu.be/hIMxZyWw6Ug #behavioralinterview datasciencecareer #interviewtips #datasciencemockinterview #interviewquestion #careeradvice

Most STAR answers focus only on the story. The best ones show how you made the decision. Full breakdown to approach STAR method here: https://youtu.be/dRqN4BuhCHU One simple trick to strengthen your STAR interview answers is to clearly explain your options, trade-offs, and the reasoning behind your decision. Interviewers aren’t just evaluating what happened in your story, they’re trying to understand how you think. When you show: • the options you considered • the trade-offs between them • the decision you made • and why you chose that approach …it helps them evaluate your judgment and confidence in handling real situations. This video shares a practical tip on how to incorporate that thinking into your STAR answers so they feel stronger and more convincing in behavioral interviews. Watch the full breakdown of how to answer behavioral interview questions using the STAR method here: https://youtu.be/dRqN4BuhCHU #starmethod #behavioralinterview #techinterview #interviewtips #interviewpreparation #careeradvice #howtocracktechinterviews

Using the STAR method but still failing behavioral interviews? Most candidates follow the structure but miss what interviewers actually evaluate. Full breakdown here: https://youtu.be/dRqN4BuhCHU Many candidates know the STAR method but still struggle to demonstrate impact. Knowing the STAR structure alone doesn’t guarantee a strong behavioral answer. Interviewers evaluate judgment, ownership, and results. In fact, research in hiring and organizational psychology shows that structured behavioral interviews are among the most predictive hiring tools, with studies often citing ~55% predictive validity compared to much lower rates for traditional unstructured interviews. But that only works when candidates actually communicate their experience clearly. The problem? Most people follow the STAR framework mechanically. They spend too much time describing the situation, gloss over the actions they personally took, and rarely quantify the results or impact. That’s why many otherwise solid STAR answers still fall flat in interviews. This video breaks down how to use the STAR method the way interviewers expect, focusing on ownership, decision-making, and measurable outcomes instead of just storytelling. If you’re preparing for behavioral interviews, this clip will help you understand why STAR answers fail and how to structure them in a way that actually impresses interviewers. This video is for: • Candidates preparing for behavioral interviews • Data science, analytics, product, and tech interview prep • Anyone struggling to make their STAR answers sound impactful • People who want to understand what interviewers actually expect Watch the full video here: https://www.youtube.com/watch?v=dRqN4BuhCHU #starmethod #behavioralinterview #interviewtips #behavioralquestions #jobinterview #interviewskills #careeradvice

Struggling with behavioral interview questions? The STAR method approach will change how you answer them. Full breakdown with examples here: https://youtu.be/dRqN4BuhCHU In this short, learn how to use the STAR framework in under 20 seconds, beyond STAR = Situation • Task • Action • Result Perfect for: • Data science interviews • Product management interviews • Software engineering interviews • Behavioral interview questions and more! #interviewtips #starmethod #careeradvice #behavioralinterview #interviewpreparation

What does a real data science case interview actually look like? In this mock interview, Jay Feng (Co-Founder of Interview Query) presents a product analytics scenario: A telecom company launches an unlimited data plan after 8 pm, and certain users suddenly consume 10x more data than average. The challenge? Distinguish between fraud, product abuse, and legitimate high usage, and determine how the findings should shape product and policy decisions. Vatsal, a data and analytics professional, walks through his approach live. Jay follows up with follow-up questions, just like a real interviewer would, pushing for clearer definitions, stronger business framing, and more structured reasoning. In this video, you’ll see: - How to approach an open-ended data science interview question - How to differentiate fraud vs product abuse using data signals - How to think through precision, recall, and false positive tradeoffs - How to connect machine learning metrics to business impact - How product and pricing decisions are influenced by analytics - The importance of structuring answers clearly under pressure - Real-time interviewer feedback on what worked and what didn’t This is not a scripted explanation. It’s a realistic mock interview that shows how technical knowledge, business reasoning, and communication come together in product analytics and data science case interviews. If you’re preparing for: - Data science interviews - Product analytics interviews - Machine learning case rounds - Fraud detection interview questions - Open-ended product case interviews This walkthrough will help you understand how to structure stronger, more decisive answers. Subscribe for more mock interviews, case breakdowns, and real interview practice. 🔥 Try Interview Query for your tech interview prep. Use code "datasciencejay" for 10% off: https://www.interviewquery.com/pricing?utm_source=youtube&utm_medium=video&utm_campaign=vatsal_mock_interview 🤖 Prepare your answers with our AI Interviewer: https://www.interviewquery.com/ai-interview?utm_source=youtube&utm_medium=social ❓ Practice top behavioral interview questions: https://www.interviewquery.com/questions?utm_source=youtube&utm_medium=social 📚 Explore company interview guides: https://www.interviewquery.com/companies?utm_source=youtube&utm_medium=social #DataScienceInterview #ProductAnalytics #CaseInterview #DataScienceMockInterview #InterviewPreparation #FraudDetection #MachineLearningInterview #Verizon #TelecomFraud

The STAR method interview is used in nearly every behavioral interview, but most candidates use it incorrectly. In this video, you’ll learn how to structure STAR answers that clearly show impact, ownership, and decision-making so you can pass behavioral interview rounds with confidence. Behavioral interview questions often determine 30–50% of hiring decisions in tech roles. The difference between getting rejected and getting an offer often comes down to how well you communicate your experience using the STAR method (Situation, Task, Action, Result). 🔥 Try Interview Query for your tech interview prep. Use code "datasciencejay" for 10% off: https://www.interviewquery.com/pricing?utm_source=youtube&utm_medium=video&utm_campaign=star_method_2026 By the end of this video, you’ll know: • How to structure a strong STAR method answer • Why most STAR interview answers fail • What interviewers actually look for in behavioral questions • How to turn weak examples into high-impact responses • A practical STAR method “cheat sheet” you can use in any interview We also break down real examples of weak vs strong behavioral interview answers so you can see exactly what works and what doesn’t. Whether you're preparing for data science, software engineering, product management, analytics, or consulting interviews, mastering behavioral interview questions is critical. Use this video as your STAR method cheat sheet to structure answers that stand out. Common behavioral interview questions include: - “Tell me about a time you handled conflict.” - “Describe a challenging project.” - “Tell me about a time you showed leadership.” - “Explain a mistake you made and what you learned.” 🤖 Prepare your answers with our AI Interviewer: https://www.interviewquery.com/ai-interview?utm_source=youtube&utm_medium=social ❓ Practice top behavioral interview questions: https://www.interviewquery.com/questions?utm_source=youtube&utm_medium=social 📚 Explore company interview guides: https://www.interviewquery.com/companies?utm_source=youtube&utm_medium=social #STARMethod #BehavioralInterviewQuestions #STARInterview #HowToAnswerSTARInterviewQuestions #InterviewPrep #TechInterviews #DataScience #SoftwareEngineering #ProductManagement #CareerAdvice

How AI Is Fixing What’s Broken in Education Education has been broken for a long time — expensive programs, massive overhead, and unsustainable business models that rely on heavy marketing and venture capital just to survive. In this YouTube Short, we explain how AI is finally solving education’s core problems by replacing outdated systems with personalized learning at scale. Instead of hiring hundreds of instructors, AI can now deliver adaptive coursework, instant feedback, and one-on-one style tutoring at a fraction of the cost. This shift means education no longer needs $15,000 boot camps or bloated operations to function. AI allows platforms to deliver higher-quality learning experiences while remaining sustainable, affordable, and accessible to far more people. The economics that once destroyed boot camps are being replaced by smarter, scalable technology. If you’re interested in the future of education, AI-powered learning, online courses, or how technology is reshaping skill development, this short breaks down why this change is happening now — and why it’s different this time. Subscribe for more insights on AI, education, startups, and the future of learning. #ai #shorts #aiengineer #ytshorts

Why AI Bootcamps Are Being Replaced by Personalized AI Learning Traditional boot camps are being disrupted — not by better marketing, but by better technology. In this YouTube Short, we explain how modern AI-powered programs are delivering personalized education at a fraction of the cost of traditional boot camps. By integrating AI directly into the curriculum, students can work on customized projects based on their background, receive instant feedback, and use AI tutors to answer questions throughout the learning process. The real breakthrough isn’t just affordability — it’s personalization. Today’s AI allows learners to shape what they want to build, how they learn, and how fast they progress. The same technology that’s transforming software development and coding workflows is now revolutionizing how education itself is delivered. Instead of one-size-fits-all programs, AI enables adaptive learning paths that scale without massive overhead. If you want to be part of the exponential growth curve of artificial intelligence, LLMs, and modern AI development, this short explains why now is the best time to learn — and why the future of education looks completely different than it did just a few years ago. Subscribe for more insights on AI, technology, startups, and the future of learning.

Take home assignments are not going away, but they are changing fast because AI is now part of everyone’s workflow. Many companies assume every candidate will use AI during take homes, so the bar is shifting from pure implementation to practical problem solving and clear communication. In this video, we break down how take homes have evolved from leetcode style puzzles into realistic tasks like extending a small app, designing a feature, or simulating real team work. Some startups are even running paid on site simulations for 2 to 3 days to see how you operate in real life. You will also see why more take homes now end with a presentation, where your reasoning and decisions matter as much as the output. If you want a simple strategy for take homes in 2025, focus on: practical execution, smart AI usage, and a crisp story for why you did what you did. #takehomeassignment #aiinterviews #interviewprep #techjobs #datascience

AI is no longer just a nice to have skill in tech. It is becoming part of the job itself across engineering, data, and analytics roles. In this video, we break down how AI engineering, machine learning, and data focused roles are growing rapidly while traditional software engineering roles stay mostly flat. Companies are not hiring more people overall. They are shifting demand toward builders who can work with AI, cloud systems, and large language models as part of their day to day work. If you are navigating the tech job market, this explains why job descriptions look different, why AI shows up everywhere, and what skills matter most if you want to stay competitive in 2025 and beyond. #aijobs #techjobs #datascience #machinelearning #careeradvice

Technical interviews in the AI era are no longer just about writing clean SQL or Python. Companies are now evaluating candidates on two levels: hands-on execution and how effectively they reason with and use AI. In this video, we explain the dual skillset companies are hiring for today. You still need strong fundamentals like debugging, system design, and implementation. But you are also expected to use AI as part of your workflow, from prompting and evaluation to research and faster decision-making. The best candidates know how to integrate AI without losing ownership of quality. If you are preparing for data science, analytics, or software engineering interviews, this shift changes how you should practice and how interviewers judge performance. #aiinterviews #techinterviews #datascience #softwareengineering #careeradvice