Yury Kashnitsky, Ph.D.’s Post

Navigating the interview process for Data Science roles can be daunting. Check out these invaluable resources shared by Yury Kashnitsky, Ph.D., which helped him through 48 interviews. A must-read for anyone preparing for their next big opportunity! #DataScience #InterviewTips #CareerGrowth

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This is pure gold for people preparing for senior Applied ML roles. My takeaways: 1. Behavioral - this is the most common interview type you must do. It is also easy to fail. Be prepared. Please, invest time in this. Prepare your stories and be ready to pitch anytime. 2. Live coding. Make sure you can confidently solve easy and medium. Hards are too much investment as you will have other things to cover.

🚀 Key Resources for Machine Learning Interviews at FAANG Companies Hey LinkedIn community! 🌟 I recently interviewed with Amazon for an Applied Scientist position. The experience was enlightening and challenging. To help others aspiring for similar roles, especially in FAANG companies, I want to share the resources that significantly boosted my preparation for Machine Learning interviews. Machine Learning: Book: "Machine Learning with PyTorch and Scikit-Learn" by Sebastian Raschka, PhD [https://a.co/d/41dRXhZ] This book provided an ideal blend of theory and practicality, covering model design, limitations, and extensive coding practices in Scikit and PyTorch. It dives deep into ML, DL, LLM foundation, GANs, Graph NNs, and RL - perfect for understanding the principles behind AI models. Book: "Ace the Data Science Interview" by Nick Singh 📕🐒 [https://a.co/d/i6AKNQj] Essential for brushing up on statistics, probability, product sense, and hypothesis testing. It's particularly valuable for its extensive coverage of SQL and real case studies, mirroring questions often asked in FAANG interviews. Youtube channel - StatsQuest by Joshua Starmer PhD For a more visual understanding of ML models, this channel breaks down complex concepts into easily digestible content. Book: "Designing ML Systems" by Chip Huyen [https://a.co/d/guEq0rs] An indispensable guide to ML system design, discussing key considerations and best practices. YouTube Channels: Exponent and Jay Feng Excellent material featuring mock ML interviews, offering a real-world view on tackling interview challenges effectively. Coding Resources: Kunal Kushwaha's YouTube playlist is superb for mastering Data Structures and Algorithms, while Navdeep Singh's Neetcode 150 list offers a roadmap to practice interview-style coding questions. Behavioral and Leadership Principles: Behavioral and Leadership Preparation: Exponent and GG The Amazon Interview Whizz have great youtube channels covering content with mock behavioral interviews to tackle scenario based questions and to resonate key leadership principles. 🔑 Pro Tips: -Research your interviewers and their work. -Use examples in your answers for clarity. -Familiarize yourself with the team's research for relevant case studies. -Communicate clearly with your recruiter for additional resources and guidance. -For coding, emphasize clarity, efficiency, and communication. -Be prepared for a range of coding questions, including those on probability, stats, and ML. -Use structured frameworks for answering questions (STAR for behavioral, can be extended to ML case study answers as well with some modification). -Thoroughly review your resume projects. -Practice mock interviews on platforms like Pramp. -Understand the company's mission and culture through their website and job description. If you've used different materials, feel free to add them in the comments! #MachineLearning #FAANGInterviews #CareerDevelopment #JobPreparation 🚀📚🔍🤖

🔍 Why Understanding Fundamentals is 𝐊𝐞𝐲 in Machine Learning Interviews I wanted to share a lesson I learned the hard way during a machine learning interview and why understanding the fundamentals is so crucial. I was asked how to handle imbalanced classes in a user-click dataset. My quick response was "oversampling" and "undersampling" – typical solutions you'll find in many prep questionnaires and ML interview prep courses. But I missed a crucial context. Resampling can significantly change the dataset distribution and make your model biased. The right approach? It depends on the problem, but if the dataset is large enough, you might avoid sampling altogether to prevent altering the dataset distribution. In fact, in most real-world applications, imbalanced datasets are almost guaranteed due to the nature of the problem space, and resampling is rarely applied. This experience taught me that interviewers often look for deep understanding, not just rehearsed answers. Here’s what you should do next time you read a prep questionnaire: 1️⃣ 𝐀𝐬𝐤 𝐘𝐨𝐮𝐫𝐬𝐞𝐥𝐟: Is this solution universally applicable? In which cases its not. 2️⃣ 𝐅𝐢𝐧𝐝 𝐂𝐨𝐮𝐧𝐭𝐞𝐫𝐞𝐱𝐚𝐦𝐩𝐥𝐞𝐬: Revise your fundamentals or ask questions on ML communities like r/MachineLearning on Reddit. 3️⃣ 𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐂𝐡𝐚𝐭𝐆𝐏𝐓: Ask about the drawbacks of a particular solution. Start with prompts like “What are the drawbacks of resampling for handling imbalanced datasets?” But remember, don’t rely solely on it, as it can sometimes provide incorrect answers. In my latest newsletter, I break down these core areas and share tips on how to prepare for different ML interview rounds. 🚀 Ready to dive deeper and ace your ML interviews? Check out my playbook here: https://lnkd.in/gdZR7wQk #MachineLearning #AI #InterviewPrep #MLInterviews

The traditional interview process can fall short when hiring a Machine Learning developer. What's the problem? Standard questions may not reveal the critical thinking skills and creativity essential for success in ML. I've fine-tuned a process to peel back the layers of tech-speak and reveal solid skills. This is your guide: #machinelearning #interview #developer #datascience #ai https://lnkd.in/df6MVVeQ

Unlock the secrets to acing your machine learning interviews in 2024 with our latest guide! Dive deep into the top machine learning interview questions and elevate your career with confidence. https://lnkd.in/dEdBevgP Follow InterviewZilla for cutting-edge tech updates and invaluable career insights. #MachineLearning #interviewprep #careergrowth #ml #mlalgorithms #ai #mlengineer #mljobs #interviewtips #interviewsuccess #linkdinpost #2024hiring #2024vision #2024goals

If your company is hiring Machine Learning talent, you want to read this. Recently, we came across a story from an ML Engineer who had gone through a gruelling interview process. He shared how he was unexpectedly hit with a LeetCode-style DSA round after spending weeks preparing for ML concepts and system design questions. Despite performing well in other areas, he was caught off guard and felt the interview process was disjointed and misaligned with the actual job requirements. Expectedly, he didn’t get the job. This story is just one of many we've encountered while researching the motivators of different types of talent through our platform, STIMULAI. If you're hiring ML talent, here are some insights to consider, grounded in reality. 1. Streamline Skill Requirements: The breadth of skills required for ML roles is enough to make anyone's head spin. From DSA, Python/SQL, system design, to ML concepts, statistics, and even DevOps – it's never-ending. Focus on the core competencies that are truly essential for the role you're hiring for. No one wants to be a jack of all trades, master of none. 2. Transparent and Predictable Processes: Many candidates feel like they're on a rollercoaster ride with unexpected interview rounds. Surprise LeetCode problems? No, thank you. Clearly communicate the interview stages and what will be tested at each stage. This helps candidates prepare better and saves everyone a lot of unnecessary stress. 3. Align Interview Content with Job Realities: There's often a significant gap between the complexity of interview questions and actual job tasks. If the job primarily involves practical application, focus more on real-world problem-solving skills rather than abstract theory. Let’s face it, no one wants to spend hours on theory when they'll be updating database rows in reality. 4. Provide Consistent and Constructive Feedback: Candidates frequently report receiving feedback that’s about as useful as a chocolate teapot. Constructive feedback helps candidates improve and shows your commitment to a fair hiring process. Be specific about areas of improvement and strengths. 5. Understand the Evolving Role Definitions: The role of an ML Engineer can mean different things to different companies – from MLOps and infrastructure to deep model development. Clearly define the focus of the role within your organisation to attract the right candidates. With the global demand for AI and machine learning soaring – and everyone and their dog competing for top ML talent – it’s crucial to ensure your hiring process stands out. ML talent plays a critical role in driving innovation and maintaining a competitive edge in today's world. By considering these insights, you can not only attract the best talent but also enhance your company's reputation as a fair and desirable workplace. #MachineLearning #Hiring #InterviewTips #TechRecruitment #TalentAcquisition #EmployerBranding

🚀 Excited to share my latest project: a robust "Resume Screening App" developed using Python, AI, and NLP! 🌐💼 🔧 Key Technical Highlights: Data Preprocessing: Cleaned and organized resume data to ensure optimal accuracy and efficiency in analysis. Feature Extraction with TF-IDF: Leveraged Term Frequency-Inverse Document Frequency to extract essential features for comprehensive analysis. Machine Learning Model Training: Trained a high-performance classification model to precisely evaluate applicant skills against job requirements. Streamlit Integration: Built a sleek and user-friendly interface using Streamlit for seamless interaction and real-time insights. Automated Resume Analysis: The app autonomously analyzes resumes, predicting the most suitable job category through advanced ML techniques. Insights for Recruiters: Provides valuable insights, empowering recruiters with data-driven decision-making during the hiring process. 🔍 This project aims to revolutionize the traditional hiring process by automating and enhancing resume screening with cutting-edge technology. Excited to share more about the technical intricacies and showcase the power of AI in recruitment! 🤖✨#Python #MachineLearning #NLP #Streamlit #ResumeAnalysis

Introducing the Resume Category Classifier, a tool designed to automatically classify resumes into predefined categories with high accuracy. Here's a breakdown of the process: 1️⃣ Text Data Preprocessing: We start by cleaning and preprocessing the resume text data using the NLTK library. This step includes removing stopwords, stemming, and other text normalization techniques to ensure the data is clean and ready for analysis. 2️⃣ Vectorization with TF-IDF: The preprocessed text data is then converted into vectors using the Term Frequency-Inverse Document Frequency (TF-IDF) method. This transformation allows us to represent the textual data in a numerical format that can be effectively used for classification. 3️⃣ Supervised ML Training: We train the model using a supervised machine learning algorithm. This involves feeding the labeled data into the algorithm so it can learn to distinguish between different resume categories based on the features extracted in the vectorization step. 4️⃣ User Interface with Streamlit: To make the tool user-friendly, I have implemented an interactive web interface using Streamlit. Users can easily upload resumes, and the system will automatically classify them into categories such as 'Software Development', 'Data Science', 'Marketing', etc. This project showcases the practical application of natural language processing and machine learning, aiming to make the resume sorting process more efficient and effective for recruiters and HR professionals. I am eager to hear any feedback or suggestions you might have. Let's innovate and transform the recruitment process together! 💼🤖 #DataScience #MachineLearning #NLP #Recruitment #AI #Streamlit #Python #CareerDevelopment #Innovation