How to land an Applied Scientist role: My tips and tricks

🎓 My Journey Into AI Engineering After finishing my degree, I wasn’t sure where to go next. Countless hours of research, advice from mentors, and some deep self-reflection eventually led me to one clear passion: Artificial Intelligence. 🌱 How I Started Even though I had prior software development experience, I chose to start from scratch. Why? Because strong foundations are non-negotiable in AI. 🗺️ My Roadmap (Step by Step) 🐍 Python Fundamentals 🧮 Data Structures & Algorithms �� Mathematics & Statistics – building the core for ML algorithms 🔢 NumPy & Pandas – mastering data manipulation & numerical computing 📈 Data Visualization – telling stories with data 🗄️ Databases (SQL/NoSQL) – handling large-scale ML datasets 🤖 Machine Learning – I realized that the formulas I once memorized—like probability, linear algebra, and calculus fundamentals—aren’t just theory. They form the backbone of machine learning libraries: 🧠 Deep Learning - ANN, CNN, RNN: Neural network foundations - LSTM & GRU: Sequence modeling - Real projects: Next-word prediction & more 🔥 Transformers – backbone of LLMs, tough but game-changing 🚀 Generative AI – LangChain, LangGraph, LangSmith, MCP & beyond Every day, I fall more in love with AI. 📚 My Learning Repository I’ve documented my complete journey—from basic Python notebooks to advanced AI projects—: 👉 https://lnkd.in/gvGS5h2a 🌟 A Message for Anyone Starting Out It’s okay not to know your path right after graduation. Explore, research, talk to people, and discover what excites you. Start with the basics—because the journey is absolutely worth it! 🔭 What’s Next? Currently working on production-ready AI projects and open to opportunities where I can apply my skills to real-world challenges. #AIJourney #MachineLearning #CareerTransition #Learning #AI #Python #DeepLearning #Transformers #GenerativeAI #OpenToWork #TechCommunity #ArtificialIntelligence #AICommunity

💡 How Domain Knowledge Can Supercharge Your ML Researcher Application Having hired in the machine learning space since 2014, I’ve seen one thing consistently separate great candidates from good ones. 👉 Domain knowledge. It’s not just about knowing models or architectures — it’s about understanding the problem space. When you’ve worked with real data, seen the bottlenecks, and felt the pain points first-hand — you bring something extra to the table: context. That context helps you: ✅ Ask smarter research questions ✅ Design better experiments ✅ Communicate results that actually matter to stakeholders So if you’re preparing for an ML Researcher interview and don’t yet have deep domain knowledge — don’t panic. Start learning the domain. Play with relevant datasets. Explore real-world problems. Because trust me — when you walk into that interview with a solid grasp of the field… you’ll instantly stand out. #MachineLearning #CareerAdvice #MLResearch #Hiring #DataScience

Looking for solid resources on Machine Learning System Design I’ve been diving deep into ML System Design lately — following a mix of YouTube videos and reading “Inside the Machine Learning Interview” by Peng Shao. While it’s a great start, I’m realizing it only scratches the surface. I’d love to go beyond interview prep and really understand end-to-end ML system design — from data pipelines and model serving to scaling, monitoring, and evaluation in production. If you’ve come across any high-quality courses, books, blogs, or real-world project breakdowns that helped you master ML system design, I’d really appreciate your recommendations. Also open to: Practical case studies or GitHub repos that show full ML pipelines. Long-form technical talks or system deep-dives from industry engineers. #MachineLearning #SystemDesign #MLOps #AIEngineering #GenerativeAI

INTERVIEW EXPERIENCE #4 Organization: MSD Role: Manager – Data Science Round 1: The interview began with a brief introduction about my background and experience. The discussion quickly moved into explaining an end-to-end machine learning project I had worked on. The panelists were particularly interested in understanding the complete ML lifecycle, from data collection and model development to deployment and monitoring. Next came the coding round, where I was asked to solve a typical LeetCode-style problem: Write a Python program to find the longest sequence in a list. Towards the end, they also explored my experience with Generative AI and related applications. Round 2: This round focused on a case study, where I was asked to present how Generative AI could be applied in a commercial pharma setting. The discussion revolved around my understanding of GenAI concepts, use cases and business impact within the pharmaceutical domain. Thanks for reading! Follow #rajreacts for more real interview experiences and insights #InterviewPrep #DataScience #GenerativeAI #MachineLearning #PharmaAI

💡 Most Asked ML/AI Questions for 20–30 LPA Roles If you’re targeting ML Engineer, Data Scientist, or Applied Scientist roles in the 15–30 LPA range, here’s the reality: Interviewers aren’t just looking for clean notebooks or flashy demos. They want to see: ● How you design experiments ● How you interpret results ● How your recommendations connect to product and business outcomes That’s why I’ve curated a list of 30+ real interview questions from leading AI and product-driven companies. What’s covered: ML Fundamentals & Math ● Supervised vs unsupervised learning, bias-variance tradeoff, regularization, overfitting vs underfitting ● Probability, statistics, and linear algebra essentials Feature Engineering & Data Preprocessing ● Encoding, scaling, leakage prevention, imputation, outlier handling ● Dataset splitting strategies and avoiding target leakage Model Evaluation & Experimentation ● Cross-validation design, offline vs online metrics, confidence intervals ● Task-specific metrics: ROC AUC, PR AUC, F1, RMSE, MAP@K ML System Design & MLOps ● Training and serving pipelines, feature stores, batch vs real-time inference, latency SLAs ● Monitoring, drift detection, retraining triggers, A/B testing, canary releases Python for ML & Deep Learning ● NumPy, pandas, scikit-learn for classical ML ● PyTorch or TensorFlow for deep learning, checkpoints, mixed precision, deployment Generative AI & LLMs ● Prompt design, function calling, RAG with embeddings and vector stores ● Fine-tuning, preference optimization trade-offs, evaluation beyond accuracy Business Case Studies & ROI Storytelling ● Metrics that matter: conversion lift, retention, CAC to LTV ● Communicating trade-offs among accuracy, latency, privacy, and cost This prep is all about practical impact and advanced problem solving—exactly what high-paying ML/AI roles demand. For more job postings related to AI, Front-end Development, or Full Stack Development, follow Sachin Patil. #MachineLearning #AI #DataScience #ML #DeepLearning #MLOps #Python #GenerativeAI #LLM #CareerGrowth #TechJobs #JobPrep

Build a Successful Career in Machine Learning 🤖✨ Machine Learning is shaping the future of technology, offering high-paying roles, diverse career paths, and massive growth potential. From mastering data science and neural networks to building real-world ML projects—discover expert tips to launch and thrive in your ML career. 🚀 𝐋𝐞𝐚𝐫𝐧 𝐌𝐨𝐫𝐞 👉 https://lnkd.in/djSUW4sv #MachineLearning #AI #ArtificialIntelligence #DataScience #TechCareers #AIJobs #FutureOfWork #MLEngineer #AIInnovation #DeepLearning #Python #CareerGrowth #AICertification #Automation #AIForEveryone

Navigating the rapidly evolving AI and Machine Learning ecosystem requires clarity not just on emerging technologies but also on the diverse #CareerRole, #TechnicalSkills, and tools shaping this space. This infographic offers a strategic overview of critical AI & ML job profiles—from #AIEngineer and #MachineLearningEngineer to #DataScientist and #BIAnalyst. It helps decode the core competencies-such as programming mastery (Python, Java, R), data science fundamentals, advanced mathematics, and hands-on experience with key frameworks like TensorFlow, PyTorch, and Hadoop. Understanding these interconnected components is vital for professionals and aspirants aiming to position themselves effectively in a competitive market. It’s more than a #careerguide; it’s a #roadmap to harnessing AI’s transformative power with practical expertise. Delving into the technical nuances of each role equips you to tailor your learning path, experiment with the right tools, and ultimately contribute meaningfully to AI-driven innovation. For anyone passionate about technology, this is a crucial starting point to grasp where your skills fit and how to leverage them in the future of work. 📩 Enquire now: zeeshan.anwar@dypvp.edu.in 📞 +91 96379 11553 / +91 9921791252 #CareerGrowth #GlobalCertification #TechCareers #ITCareer #AIML

Built multiple ML models… but still not getting interview calls? You’re not alone — and here’s why most ML enthusiasts get stuck. 🚫 They focus only on algorithms, not outcomes. 🚫 They never deploy models in real-world environments. 🚫 They skip building a strong portfolio to showcase their work. Here’s the truth: Machine Learning isn’t just about building models — it’s about solving problems, deploying solutions, and demonstrating impact. When you combine your technical skills with practical exposure… 🔓 Static Projects → Live Industry Implementations ⏳ Endless Learning → Hands-on Deployment Experience 📉 Missed Opportunities → Recruiter-Ready Portfolio And once you showcase your end-to-end ML journey — ✅ Recruiters see your real value ✅ You build industry trust ✅ Your career accelerates with purpose So tell me — if you had to choose your next move: Keep building random models OR master real-world ML deployment and portfolio building — which are you choosing? 💬 Drop your answer below! Want a proven path to get noticed? Comment “ROADMAP” and I’ll share our Data Science Career Roadmap Checklist to help you land your dream job. #DataScience #MachineLearning #CareerGrowth #ResumeHubs #MLProjects #DataScienceCareer #AI #JobReadySkills #PortfolioBuilding #TechCareers #HireReady #LearnWithResumeHubs

Built multiple ML models… but still not getting interview calls? You’re not alone — and here’s why most ML enthusiasts get stuck. 🚫 They focus only on algorithms, not outcomes. 🚫 They never deploy models in real-world environments. 🚫 They skip building a strong portfolio to showcase their work. Here’s the truth: Machine Learning isn’t just about building models — it’s about solving problems, deploying solutions, and demonstrating impact. When you combine your technical skills with practical exposure… 🔓 Static Projects → Live Industry Implementations ⏳ Endless Learning → Hands-on Deployment Experience 📉 Missed Opportunities → Recruiter-Ready Portfolio And once you showcase your end-to-end ML journey — ✅ Recruiters see your real value ✅ You build industry trust ✅ Your career accelerates with purpose So tell me — if you had to choose your next move: Keep building random models OR master real-world ML deployment and portfolio building — which are you choosing? 💬 Drop your answer below! Want a proven path to get noticed? Comment “ROADMAP” and I’ll share our Data Science Career Roadmap Checklist to help you land your dream job. #DataScience #MachineLearning #CareerGrowth #ResumeHubs #MLProjects #DataScienceCareer #AI #JobReadySkills #PortfolioBuilding #TechCareers #HireReady #LearnWithResumeHubs

Top 10 Interview Questions for AI/ML Engineers – Day 2 Continuing my 10-day series of important interview questions for AI/ML Engineer, Data Scientist, and GenAI Developer roles. Here are today’s 10 questions 👇 Day 2 – Advanced ML, Cloud & System Design 1. Explain the difference between API vs MCP when integrating tools with LLM agents. 2. What is augmentation in RAG (Retrieval-Augmented Generation) and why is it important? 3. How do you convert an image into grayscale in CNNs and why is it useful? 4. Explain how LLMs process images, audio, and video even though they work with text at the core. 5. What is CI/CD in MLOps, and why is it important? 6. What is the role of unit testing in automation pipelines? 7. Explain the purpose of lemmatization in NLP with an example. 8. What are the pros and cons of using APIs vs MCP for LLM-based agents? 9. What is the best time complexity achievable for linear search? 10. In REST APIs, which protocol is used and why? Which one do you think is most underrated in interviews? Drop your thoughts 👇 📌 If you missed my Day 1 post, go check it out! #MachineLearning #DataScience #AIML #GenAI #InterviewPreparation #Python #MLOps #DeepLearning #LLM #CareerGrowth #CloudComputing #BigData #AICommunity #TechCareer #SQL #CodingInterview #SoftwareEngineering #DataScience #AIInterviewPrep #MLInterviews