Data Science Jobs

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Remote Data Systems Analyst https://www.aplitrak.com/?adid=bmluYXNvcmtpbi4wODM3NC41NDAzQHZhY28uYXBsaXRyYWsuY29t 👉WhatsApp Channel: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46 👉Telegram Channel: https://t.me/dataanalyticsbuddy Like for more such updates ❤️ Wishing you lots of success in your career👍👍

Divya Staffing Solution Data Scientist Exp: Fresher https://www.linkedin.com/jobs/view/4132682181

𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍 1) Generative AI 2) Big data artificial intelligence 3 ) Microsoft Al for beginners 4) Prompt Engineering for Chat GPT 𝐋𝐢𝐧𝐤👇 :-  https://pdlink.in/40Fbg9d Enroll For FREE & Get Certified🎓

Google's Applied ML team is hiring: Hiring for skilled ML engineers in Bangalore to work on edge optimization and acceleration of GenAI and non-GenAI models for google's Edge TPU. For more information about the opportunity and application, please refer to the following link:https://www.google.com/about/careers/applications/jobs/results/76351911959110342

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝗯 𝗢𝗽𝗲𝗻𝗶𝗻𝗴𝘀 𝗜𝗻 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 😍 Mynta :- https://pdlink.in/4jBs89K American Express :- https://pdlink.in/4hgaarB KLA :- https://pdlink.in/4jEnhEA BCG :- https://pdlink.in/4hl4RHx Accenture :- https://pdlink.in/3WZEpv9 Siemens :- https://pdlink.in/42s5Dhh Apply before the link expires 🏃‍♂️

BCG is Hiring for DATA SCIENTIST Role:- DATA SCIENTIST Qualifications:- GRADUATION Experience:- Fresher's and Experienced Mode:- WORK FROM OFFICE Apply Now:- https://careers.bcg.com/global/en/job/BCG1US26309EXTERNALENGLOBAL/Data-Scientist-India-BCG-X 👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226 👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5 Like for more ❤️

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1. What is the Impact of Outliers on Logistic Regression? The estimates of the Logistic Regression are sensitive to unusual observations such as outliers, high leverage, and influential observations. Therefore, to solve the problem of outliers, a sigmoid function is used in Logistic Regression. 2. What is the difference between vanilla RNNs and LSTMs? The main difference between vanilla RNNs and LSTMs is that LSTMs are able to better remember long-term dependencies, while vanilla RNNs tend to forget them. This is due to the fact that LSTMs have a special type of memory cell that can retain information for longer periods of time, while vanilla RNNs only have a single layer of memory cells. 3. What is Masked Language Model in NLP? Masked language models help learners to understand deep representations in downstream tasks by taking an output from the corrupt input. This model is often used to predict the words to be used in a sentence. 4. Why is the KNN Algorithm known as Lazy Learner? When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.

𝟱 𝗙𝗥𝗘𝗘 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 Ready to dive into the world of Machine Learning? Here are 5 powerful resources that will guide you every step of the way—from beginner concepts to advanced techniques. 𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/40wyXk8 Enroll For FREE & Get Certified🎓

AB InBev is hiring! Position: Machine Learning Engineer Qualification: Bachelor’s/ Master’s Degree Salary: 12 - 20 LPA (Expected) Experience: Freshers/ Experienced Location: Work From Home/ office 📌Apply Now: https://ab-inbev-gcc.sensehq.com/careers/jobs/54769

Citi is hiring! Position: Data Scientist Qualifications: Bachelor’s Degree Salary: 12 - 18 LPA (Expected) Experience: Freshers (0 - 2 Years) Location: Bengaluru, India (Hybrid) 📌Apply Now: https://jobs.citi.com/job/-/-/287/76402677072 👉WhatsApp Channel: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46 👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226 👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5 Like for more ❤️

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SAP is Hiring for ASSOCIATE - DATA SCIENTIST" Role:- ASSOCIATE - DATA SCIENTIST Qualifications:- GRADUATION Mode:- WORK FROM OFFICE CTC:- 10 LPA Location:- BANGALORE, KARNATAKA Apply Now:- https://jobs.sap.com/job/Bangalore-Associate-Data-Scientist-KA-560066/1163936701/ 🚀 Join the WhatsApp Group for more job updates and pass this information with your friends and groups 🚨 Join Now:- https://whatsapp.com/channel/0029VaxTMmQADTOA746w7U2P

Are you looking to become a machine learning engineer? I created a free and comprehensive roadmap. Let's go through this post and explore what you need to know to become an expert machine learning engineer: Math & Statistics Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics. Here are the probability units you will need to focus on: Basic probability concepts statistics Inferential statistics Regression analysis Experimental design and A/B testing Bayesian statistics Calculus Linear algebra Python: You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning. Variables, data types, and basic operations Control flow statements (e.g., if-else, loops) Functions and modules Error handling and exceptions Basic data structures (e.g., lists, dictionaries, tuples) Object-oriented programming concepts Basic work with APIs Detailed data structures and algorithmic thinking Machine Learning Prerequisites: Exploratory Data Analysis (EDA) with NumPy and Pandas Basic data visualization techniques to visualize the variables and features. Feature extraction Feature engineering Different types of encoding data Machine Learning Fundamentals Using scikit-learn library in combination with other Python libraries for: Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees) Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering) Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients) Solving two types of problems: Regression Classification Neural Networks: Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions. Types of Neural Networks: Feedforward Neural Networks: Simplest form, with straight connections and no loops. Convolutional Neural Networks (CNNs): Great for images, learning visual patterns. Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information. In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems. Deep Learning: Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Long Short-Term Memory Networks (LSTMs) Generative Adversarial Networks (GANs) Autoencoders Deep Belief Networks (DBNs) Transformer Models Machine Learning Project Deployment Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at: Version Control for Data and Models Automated Testing and Continuous Integration (CI) Continuous Delivery and Deployment (CD) Monitoring and Logging Experiment Tracking and Management Feature Stores Data Pipeline and Workflow Orchestration Infrastructure as Code (IaC) Model Serving and APIs Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Credits: https://t.me/datasciencefun Like if you need similar content 😄👍

𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀/𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗦𝘂𝗺𝗺𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝟮𝟬𝟮𝟱😍 Company Name:- Siemens Healthineers Position: Data Analytics/Data Science Intern Duration: 10-12 weeks Start Dates: June 2nd or June 16th, 2025 Work Type: Hybrid (in-office & remote) 𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄👇 :-  https://pdlink.in/42s5Dhh Apply before the link expires

Creating a data science portfolio is a great way to showcase your skills and experience to potential employers. Here are some steps to help you create a strong data science portfolio: 1. Choose relevant projects: Select a few data science projects that demonstrate your skills and interests. These projects can be from your previous work experience, personal projects, or online competitions. 2. Clean and organize your code: Make sure your code is well-documented, organized, and easy to understand. Use comments to explain your thought process and the steps you took in your analysis. 3. Include a variety of projects: Try to include a mix of projects that showcase different aspects of data science, such as data cleaning, exploratory data analysis, machine learning, and data visualization. 4. Create visualizations: Data visualizations can help make your portfolio more engaging and easier to understand. Use tools like Matplotlib, Seaborn, or Tableau to create visually appealing charts and graphs. 5. Write project summaries: For each project, provide a brief summary of the problem you were trying to solve, the dataset you used, the methods you applied, and the results you obtained. Include any insights or recommendations that came out of your analysis. 6. Showcase your technical skills: Highlight the programming languages, libraries, and tools you used in each project. Mention any specific techniques or algorithms you implemented. 7. Link to your code and data: Provide links to your code repositories (e.g., GitHub) and any datasets you used in your projects. This allows potential employers to review your work in more detail. 8. Keep it updated: Regularly update your portfolio with new projects and skills as you gain more experience in data science. This will show that you are actively engaged in the field and continuously improving your skills. By following these steps, you can create a comprehensive and visually appealing data science portfolio that will impress potential employers and help you stand out in the competitive job market.

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Inxite-out hiring Senior Data Scientist https://inxiteout.keka.com/careers/jobdetails/3068?source=linkedin