Data Science Jobs

Machine learning powers so many things around us – from recommendation systems to self-driving cars! But understanding the different types of algorithms can be tricky. This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning. 𝟏. 𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data. 𝐒𝐨𝐦𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞: ➡️ Linear Regression – For predicting continuous values, like house prices. ➡️ Logistic Regression – For predicting categories, like spam or not spam. ➡️ Decision Trees – For making decisions in a step-by-step way. ➡️ K-Nearest Neighbors (KNN) – For finding similar data points. ➡️ Random Forests – A collection of decision trees for better accuracy. ➡️ Neural Networks – The foundation of deep learning, mimicking the human brain. 𝟐. 𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 With unsupervised learning, the model explores patterns in data that doesn’t have any labels. It finds hidden structures or groupings. 𝐒𝐨𝐦𝐞 𝐩𝐨𝐩𝐮𝐥𝐚𝐫 𝐮𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞: ➡️ K-Means Clustering – For grouping data into clusters. ➡️ Hierarchical Clustering – For building a tree of clusters. ➡️ Principal Component Analysis (PCA) – For reducing data to its most important parts. ➡️ Autoencoders – For finding simpler representations of data. 𝟑. 𝐒𝐞𝐦𝐢-𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning. 𝐂𝐨𝐦𝐦𝐨𝐧 𝐬𝐞𝐦𝐢-𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞: ➡️ Label Propagation – For spreading labels through connected data points. ➡️ Semi-Supervised SVM – For combining labeled and unlabeled data. ➡️ Graph-Based Methods – For using graph structures to improve learning. 𝟒. 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards. 𝐏𝐨𝐩𝐮𝐥𝐚𝐫 𝐫𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞: ➡️ Q-Learning – For learning the best actions over time. ➡️ Deep Q-Networks (DQN) – Combining Q-learning with deep learning. ➡️ Policy Gradient Methods – For learning policies directly. ➡️ Proximal Policy Optimization (PPO) – For stable and effective learning. ENJOY LEARNING 👍👍

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EXL is looking for a Senior Neo4j Developer to join our growing data engineering team! 🧠 Experience Required: ✔️ 10+ years overall in software/data engineering ✔️ 4+ years of hands-on experience with Neo4j ✔️ Strong background in Python and PySpark ✔️ Experience in graph modeling, Cypher queries, and big data pipelines 🌐 Location: Open to all EXL locations [Hybrid] Join us to build cutting-edge graph-based solutions that solve real-world business problems. 📩 Interested or know someone who might be a great fit? Let’s connect! Share your resume at Qareena.Kazi@exlservice.com

Hexaware conducting Walkin Drive for AI Engineer and Lead Data Scientist (GenAI)-Hyderabad Location-24th Aug 2025(Sunday) Interested candidates share your CV at umaparvathyc@hexaware.com Open Positions: AI Engineer Lead Data Scientist (GenAI) AI Engineer Experience- 3+years Lead Data Scientist (GenAI) Experience- 7+years Notice Period- 15 days/30days Max (who serving Notice Period) Walkin Drive Location- Hyderabad Date of drive- 24th Aug 2025(Sunday) Must have Experience: LLM, Advance RAG, NLP, transformer model, LangChain Technical Skill: 1. Strong Experience in Data Scientist (GENAI) 2. Proficiency with Generative AI models like GANs, VAEs, and transformers 3. Expertise with cloud platforms (AWS, Azure, Google Cloud) for deploying AI models 4. Strong Python Fast API experience, SDA based implementations for all the APIs 5. Knowledge of Agentic AI concepts and applications

Hiring AI Solution Architect And AI Technical Lead – Full Stack & Enterprise Architecture, Zealogics, fully remote Job Title: Senior AI Solutions Architect – Generative AI & LLMs Location: Fully Remote Experience: 15+ years in enterprise AI architecture and software engineering Required Skills & Technologies: Programming: Python, .NET (C#), Node.js, React, Angular Cloud Platforms: Azure (AI Foundry, OpenAI, DevOps), AWS (Bedrock, SageMaker), GCP LLMs & GenAI: GPT-4, AI & ML Tools: Hugging Face, TensorFlow, PyTorch, Keras DevOps & CI/CD: Azure DevOps, GitHub Actions, Jenkins Security & Identity: Azure AD, SAML 2.0, Microsoft Purview, Key Vault Databases: SQL Server, PostgreSQL, Cosmos DB, MongoDB, Pinecone, Chroma ---------------------- Job Title: AI Technical Lead – Full Stack & Enterprise Architecture 📍 Location: India 🕒 Experience Required: 15+ Years 🧑‍💼 Employment Type: Full-Time, fully rmeote 🏢 Department: Technology / AI Solutions Required Skills & Qualifications 15+ years of experience in software development, with deep expertise in Full Stack technologies (e.g., .NET, React/Angular, Node.js, Python). Proven experience in architecting enterprise-grade applications and AI integrations. Hands-on experience with cloud platforms (Azure preferred), microservices, and containerization. Strong understanding of AI/ML concepts, APIs, and deployment strategies. Excellent leadership, communication, and stakeholder management skills. Experience in mentoring teams and driving technical excellence. Familiarity with compliance standards (e.g., PCI DSS, FedRAMP) is a plus. If interested, please share your CV with following details to simi@zealogics.com: Full Legal Name Current Location Permanent Location Contact E-Mail LinkedIn Notice Current CTC Expected CTC

𝟒 𝐁𝐞𝐬𝐭 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐢𝐧 𝟐𝟎𝟐𝟓 𝐭𝐨 𝐒𝐤𝐲𝐫𝐨𝐜𝐤𝐞𝐭 𝐘𝐨𝐮𝐫 𝐂𝐚𝐫𝐞𝐞𝐫😍 In today’s data-driven world, Power BI has become one of the most in-demand tools for businesses〽️📊 The best part? You don’t need to spend a fortune—there are free and affordable courses available online to get you started.💥🧑‍💻 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4mDvgDj Start learning today and position yourself for success in 2025!✅️

Company Name: Waymo Role : ML Engineer Batch : 2022/2021 and before passouts Link: https://careers.withwaymo.com/jobs/ml-compiler-engineer-compute-bengaluru-karnataka-india

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𝐆𝐄 𝐀𝐞𝐫𝐨𝐬𝐩𝐚𝐜𝐞 𝐈𝐧𝐭𝐞𝐫𝐧𝐬𝐡𝐢𝐩, 𝟐𝟎𝟐𝟓! Positio: Data Science Intern Qualification: Bachelor’s/ Master’s Degree Salary: ₹ 30,000 - ₹ 50,000 Per Month (Expected) Batch: 2024/ 2025/ 2026/ 2027 Experienc: Freshers Locatio: Bengaluru, India 📌Apply Now: https://careers.geaerospace.com/global/en/job/R5016107/DT-Data-Science-Intern 👉WhatsApp Channel: https://whatsapp.com/channel/0029VaxngnVInlqV6xJhDs3m 👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5 All the best 👍👍

Resume not working? This might be the problem I've seen hundreds of data analysts struggle to get a single interview, and I've also seen the resumes that some of my mentees made. They all say the same thing (and that is the exact reason why they come up to me and say that they're not getting calls): "I've learned Python. I've got my SQL certification. I've built dashboards in Tableau." Most of you are focusing on the tools rather than the results. Employers aren't looking for people who can build dashboards—they want to know what that dashboard does for the company. Does it save time? Boost efficiency? Cut costs? Improve sales? No: "Built a sales dashboard that improved efficiency." Yes: "Created a sales dashboard that reduced reporting time by 30%, using XYZ." It's not enough to just say you did something. Explain how you approached the problem, the decisions you made, and the outcomes you achieved. You also get extra points if you identify flaws in your work and how you solved them. That's a story. And, in resumes, you must Tell your story, not show your grocery list. Most people focus on what they did. Most companies focus on what you can do. I have curated top-notch Data Analytics Resources 👇👇 https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Hope this helps you 😊

𝟮𝟱+ 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 😍 Breaking into Data Analytics isn’t just about knowing the tools — it’s about answering the right questions with confidence🧑‍💻✨️ Whether you’re aiming for your first role or looking to level up your career, these real interview questions will test your skills📊📌 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3JumloI Don’t just learn — prepare smart✅️

McKinsey & Company is hiring Data Scientist 🚀 Experience : 2+ Years Location : Bangalore Apply link : http://www.mckinsey.com/careers/search-jobs/jobs/datascientist-95589?appsource=LinkedIn 👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J 👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5 All the best 👍👍

𝐆𝐄 𝐀𝐞𝐫𝐨𝐬𝐩𝐚𝐜𝐞 𝐈𝐧𝐭𝐞𝐫𝐧𝐬𝐡𝐢𝐩, 𝟐𝟎𝟐𝟓! Positio: Data Science Intern Qualification: Bachelor’s/ Master’s Degree Salary: ₹ 30,000 - ₹ 50,000 Per Month (Expected) Batch: 2024/ 2025/ 2026/ 2027 Experienc: Freshers Locatio: Bengaluru, India 📌Apply Now: https://careers.geaerospace.com/global/en/job/R5016107/DT-Data-Science-Intern 👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J 👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5 All the best 👍👍

Pandas Cheatsheet For Data Science

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Excel interview questions for both data analysts and business analysts 1) What are the basic functions of Microsoft Excel? 2) Explain the difference between a workbook and a worksheet. 3) How would you freeze panes in Excel? 4) Can you name some common keyboard shortcuts in Excel? 5) What is the purpose of VLOOKUP and HLOOKUP? 7) How do you remove duplicate values in Excel? 8) Explain the steps to filter data in Excel. 9) What is the significance of the "IF" function in Excel, and can you provide an example of its use? 10) How would you create a pivot table in Excel? 11) Explain the use of the CONCATENATE function in Excel. 12) How do you create a chart in Excel? 13) Explain the difference between a line chart and a scatter plot. 14) What is conditional formatting, and how can it be applied in Excel? 15) How would you create a dynamic chart that updates with new data? 16) What is the INDEX-MATCH function, and how is it different from VLOOKUP? 17) Can you explain the concept of "PivotTables" and when you would use them? 18) How do you use the "COUNTIF" and "SUMIF" functions in Excel? 19) Explain the purpose of the "What-If Analysis" tools in Excel. 20) What are array formulas, and can you provide an example of their use? Business Analysis Specific: 1) How would you analyze a set of sales data to identify trends and insights? 2) Explain how you might use Excel to perform financial modeling. 3) What Excel features would you use for forecasting and budgeting? 4) How do you handle large datasets in Excel, and what tools or techniques do you use for optimization? 5) What are some common techniques for cleaning and validating data in Excel? 6) How do you identify and handle errors in a dataset using Excel? Scenario-based Questions: 1) Imagine you have a dataset with missing values. How would you approach this problem in Excel? 2) You are given a dataset with multiple sheets. How would you consolidate the data for analysis? I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Hope this helps you 😊