Mission’s Post

The most interesting thing in AI Recruitment: Will AI render the job of Data Scientist obsolete? In my opinion - no! I think the future is extremely bright if you are a Data Scientist. The landscape is changing, sure. However, Data Scientists who have the skills that are currently in demand within AI will continue to be highly sought after. I explain my thinking in todays video. Agree? Thanks for everything and see you at work! ❤️ Chris Digitalent - AI & Machine Learning Recruitment

ML/Data Science interviews vary from company to company. Some asks ML, DL, statistics, optimization and some asks questions from whatever model you have used. Here is glimpse of some of interviews I gave recently and topics they asked: Cisco [role: ML Engineer]:- since the team I was being interviewed for was working on RAG and GenAI, so they asked questions from RAG, transformers, How LLM are trained, how to fine-tune LLM, some general ML questions like overfitting/underfitting etc. Piramal Finance [role: GenAI Engineer]:- RAG based case study and some more questions from RAG and LLM. Rakuten [role: research Scientist]:- Since the team I was being interviewed for was working on RL, so two rounds focused on Reinforcement learning [covering topics like q-learning, bellman equations, markov chain etc.], with some general questions like regularization, loss function, precision-recall etc. FYND [role: Data Scientist]:- Three ML round, one was focused on classical ML and deep learning, one focused on computer vision specifically CNN models and projects, one focused on projects but questions from diffusion models [since I had projects in diffusion] Google[role: SWE III ML]:- ML round focused on a case study on computer vision, with some model specific questions. Flipkart [role: Data Scientist]:- ML round focused on classical ML, DL, with mathematical details involving optimization, evaluation metrics, loss function related questions. One round focused on ML projects discussion end-to-end. Summary: Basic ML are always asked, rest things depends on role, responsibilities and team. Keep learning. Here are some useful links: Real insights to ML/Data Science interviews: <https://lnkd.in/g684Ngxr> Coding pattern asked in ML interviews: <https://lnkd.in/ejU_iSee> Preparing project for ML interviews: <https://lnkd.in/gR4CzKeQ> How to better prepare for ML roles: <https://lnkd.in/eRAdvRZu> Deep Learning Interview questions: <https://lnkd.in/gSwDFTMY> #datascience #ml #ai #datascience

My Data Science Interview Experience with NVIDIA Role: Deep Learning Solutions Architect I applied through one of my connections. I got a call from HR to discuss my experience and suitability for the role. --- Round 1 Focus: Deep Learning Fundamentals 1. Transformer architectures 2. Attention mechanisms and types of attention 3. Loss functions 4. Optimizers 5. Deep learning architectures for NLP and CV Since I was focusing on NLP, I answered most questions from that perspective. The interviewer was fine with this because I answered all the fundamental questions well. --- Round 2 Focus: Advanced Topics and Practical Applications 1. Retrieval-Augmented Generation (RAG) 2. Docker containerization and Orchestration 3. Deep learning architectures for CV I mentioned that I had experience in NLP, and the interviewer was okay with that. However, when the interviewer asked about CV architectures, I mistakenly talked about some projects I had worked on. This led to follow-up questions about the architectures and their comparisons. Unfortunately, I couldn’t explain them well. The interview took a sharp downturn at that point. --- Takeaways: 1. Guide the interview: Try to steer the conversation toward your strengths and areas of expertise. 2. Think before you answer: Be mindful of what you share, as it may lead to deeper questions that you should be prepared to answer.

Machine Learning (AI) roles interviews guide 🌟 The author received offer from Meta, Google, Apple and the like, and compiled this guide based on his personal experience and notes. Thanks Alireza Dirafzoon for sharing this: https://lnkd.in/gt9VQ-4d #interviewquestions #machinelearningengineer

AI isn't new, but it's definitely got a lot flashier. Dice put together this great e-book about AI careers and found that the most demand roles for AI talent are: ▪ Data Scientists ▪ Machine Learning Engineers ▪ Software Engineers ▪ Data Engineers ▪ Solutions Architects The guide also goes in depth into the skills most common on job postings for these roles, average salaries, and the most common years of experience for each role! #AIguide #AIcareer #AIskills

Questions asked during data scientist, ML engineer, AI developer, etc... role interviews are probably not what you might think. The type of questions that you might think would be asked would be difficult technical questions such as: -Can you explain the bias-variance tradeoff and how it impacts the generalization ability of a model? -Describe the backpropagation process in neural networks. How does it work, and why is it important? -Given a dataset with a large number of features, how would you select the most important features for modeling? And those are completely reasonable questions to ask because they do test a candidate's technical skills, problem-solving abilities, and theoretical knowledge. However, those aren't the questions that will be asked (at least not during the first round interview). Here are the questions that would be most likely asked and I'll tell you why: -What is an array? -What is the syntax for a dictionary? These questions test a candidates technical ability at the most fundamental level. Interviewers know that if a candidate doesn't know the fundamentals, then they won't know the advanced concepts or be able to do work the requires fundamental knowledge. And even if a candidate does know advanced ML concepts and can explain them well, if they don't know the fundamentals they likely won't be seriously considered. So if you're prepping for a data scientist or similar role, make sure you know the basics! #datascience #ai #artificialintelligence #ml #machinelearning

🚀 Breakthroughs in generative AI are fueling the rise of the emerging AI Engineering role, setting it apart from traditional data science. Do these disciplines tackle the same problems? Is there overlap in techniques and models? In this insightful video, Isaac Ke, a former data scientist turned AI engineer, delves into the key differences and similarities between these fields and highlights some of the latest trends shaping the AI landscape. Don't miss out on understanding how these roles are evolving! #AI #GenerativeAI #DataScience #AIEngineering #TechTrends #MachineLearning #ArtificialIntelligence #Innovation #TechTalks

🌟 Top AI Roles in High Demand! 🌟 Ever wondered which AI roles are taking the job market by storm? If you're delving into the world of AI & ML, now's a fantastic time to explore the most sought-after positions! Here are my top picks for 2024 so far: 1. **AI Research Scientist**: This role is at the cutting edge of technology, focusing on advancing AI models and algorithms. They're the masterminds behind the latest innovations in deep learning and neural networks. If you have a knack for research and a thirst for discovery, this might be your calling! 🧠💡 2. **Machine Learning Engineer**: The backbone of AI implementations, these professionals design, build, and deploy ML models. It’s a role where practicality meets creativity, transforming theoretical models into real-world applications. 3. **Data Scientist**: In an era driven by data, Data Scientists analyze and interpret complex datasets to help companies make informed decisions. They're essential in deciphering trends and generating actionable insights that drive business value. 📈 For anyone looking to break into these roles, having strong foundations in programming, statistics, and domain-specific knowledge is crucial. Networking and continuous learning play a massive part too—never stop growing your skills! Curious about how you can find top talent for your company? get in touch with me at max.crumpton@edisonsmart.com. #AI #MachineLearning #AIJobs

𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 & 𝐀𝐈 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐒𝐞𝐫𝐢𝐞𝐬: 15 𝐾𝑒𝑦 𝑄𝑢𝑒𝑠𝑡𝑖𝑜𝑛𝑠 𝐴𝑏𝑜𝑢𝑡 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐕𝐞𝐜𝐭𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬 (𝐒𝐕𝐌): The "Data Science & AI Interview Question Series" is a valuable resource for freshers and experienced data science professionals preparing for job interviews. It provides a comprehensive overview of Support Vector Machines (SVM), a popular machine-learning algorithm used for classification and regression tasks. The series covers key concepts, including the definition of SVM, the importance of margin, the role of support vectors, handling non-linearly separable data with kernel tricks, and the advantages and limitations of SVM. By studying this series, candidates can gain a solid understanding of SVM and effectively answer interview questions, increasing their chances of success. The document focuses on Support Vector Machines (SVM), a supervised machine learning algorithm used for classification and regression tasks. It explains the concept of margin, which is the distance between the decision boundary and the nearest data points, and how maximizing the margin leads to better generalization. It also discusses support vectors, which are the data points closest to the decision boundary and have the most influence on its position. 𝐋𝐢𝐬𝐭 𝐨𝐟 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝 𝐢𝐧 𝐭𝐡𝐞 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭: 1.   What is a Support Vector Machine (SVM)? 2.   What is the "margin" in SVM? Why is it important? 3.   What are support vectors in SVM? 4.   How does SVM handle non-linearly separable data? 5.   What is the kernel trick in SVM? Explain with an example. 6.   What is the role of the C parameter in SVM? 7.   What is the difference between SVM for classification and regression? 8.   What are the advantages of using SVM? 9.   What are the limitations of SVM? 10.   How do you evaluate the performance of an SVM model? Hope you find this insightful. Like and save for future. #SVM, #SupportVectorMachines, #MachineLearning, #DataScience, #AI, #InterviewQuestions, #KernelTrick, #Margin, #SupportVectors, #Classification, #Regression, #ModelEvaluation, #DataScienceInterviewPrep, #AIInterviewPrep, #JobInterviewTips, #DataScienceCareer

Data Science vs Artificial Intelligence vs Machine Learning: Which one is Best for Your Dream Job? The guide covers all the information you need in order to thrive in the fields of Data Science, Artificial Intelligence (AI), and Machine Learning (ML). In understanding the main differences between Data Science vs AI vs ML-and which ones are most valuable to employers today, this article will take you through every important concept that will propel your career ahead. Read full article here: https://lnkd.in/d2YHf8fs With the end of this ultimate guide, you'll gain complete knowledge on all the skills and certifications needed to reach the pinnacle of Data Science AI ML. This must be that final article that helps you prepare for interviews and make wise decisions regarding your career in AI, ML, and Data Science - You're in the right place!