Pocket FM hiring Data Scientist

Apply link: https://www.linkedin.com/jobs/view/4204079017

👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J

👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5

All the best 👍👍

Worley are actively hiring for Multiple Data Scientist positions 

Experience : 6 Years to 12 Years

Location - Navi Mumbai

Role & responsibilities


1. Hands-on programming and architecture capabilities in Python.

2. Demonstrated technical expertise around architecting solutions around AI, ML, deep learning and Generative AI related technologies.

3. Experience in implementing and deploying Machine Learning solutions (using various models, such as GPT-4, Lama2, Mistral ai, text embedding ada, Linear/Logistic Regression, Support Vector Machines, (Deep) Neural Networks, Topic Modeling, Game Theory etc. )

4. Understanding of Nvidia Enterprise NEMO Suite.

5. Expertise in popular deep learning frameworks, such as TensorFlow, PyTorch, and Keras, for building, training, and deploying neural network models.

6. Experience in AI solution development with external SaaS products like Azure OCR

7. Experience in the AI/ML components like Azure ML studio, Jupyter Hub, TensorFlow & Sci-Kit Learn

8. Hands-on knowledge of API frameworks.

9. Familiarity with the transformer architecture and its applications in natural language processing (NLP), such as machine translation, text summarization, and question-answering systems.

10. Expertise in designing and implementing CNNs for computer vision tasks, such as image classification, object detection, and semantic segmentation.

11. Hands on experience in RDBMS, NoSQL, big data stores like: Elastic, Cassandra.

12. Experience with open source software

13. Experience using the cognitive APIs machine learning studios on cloud.

14. Hands-on knowledge of image processing with deep learning ( CNN,RNN,LSTM,GAN)

15. Familiarity with GPU computing and tools like CUDA and cuDNN to accelerate deep learning computations and reduce training times.

16. Understanding of complete AI/ML project life cycle

17. Understanding of data structures, data modelling and software architecture

18. Good understanding of containerization and experience working with Docker, AKS.

If you're interested then Kindly share your updated resume at 👉 sweety.nathani@worley.com

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10 Machine Learning Concepts You Must Know

✅ Supervised vs Unsupervised Learning – Understand the foundation of ML tasks
✅ Bias-Variance Tradeoff – Balance underfitting and overfitting
✅ Feature Engineering – The secret sauce to boost model performance
✅ Train-Test Split & Cross-Validation – Evaluate models the right way
✅ Confusion Matrix – Measure model accuracy, precision, recall, and F1
✅ Gradient Descent – The algorithm behind learning in most models
✅ Regularization (L1/L2) – Prevent overfitting by penalizing complexity
✅ Decision Trees & Random Forests – Interpretable and powerful models
✅ Support Vector Machines – Great for classification with clear boundaries
✅ Neural Networks – The foundation of deep learning

React ❤️ for detailed explanation

Google hiring Data Scientist, Product, Google Home

Apply link: https://careers.google.com/jobs/results/122949546446594758-data-scientist/?src=Online/LinkedIn/linkedin_us&utm_source=linkedin&utm_medium=jobposting&utm_campaign=contract

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Whether you’re a student, fresher, or professional looking to upskill — Microsoft has dropped a series of completely free courses to get you started.

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Data Visualization Tools & Best Practices

1. Power BI:

Purpose: Powerful business analytics tool to visualize and share insights from your data.

Best Practices:

Use simple visuals (avoid overloading with data).

Choose the right chart type (e.g., bar chart for comparisons, line chart for trends).

Use slicers and filters to allow users to explore data interactively.

Keep your color schemes consistent and avoid too many colors.

Use Tooltips for additional context without cluttering the visual.


2. Tableau:

Purpose: Data visualization tool used for creating interactive and shareable dashboards.

Best Practices:

Minimize clutter by reducing non-essential elements (e.g., gridlines, unnecessary labels).

Ensure readability with a clean and intuitive layout.

Use dual-axis charts when comparing two measures in a single visual.

Keep titles and labels concise; avoid redundant information.

Prioritize data integrity (avoid misleading visualizations).


3. Matplotlib & Seaborn (Python):

Purpose: Python libraries for static, animated, and interactive visualizations.

Best Practices:

Use subplots to visualize multiple charts together for comparison.

Keep axes readable with appropriate titles and labels.

Choose appropriate color palettes (e.g., Seaborn has good built-in color schemes).

Annotations can help clarify key points on the chart.

Use log scaling for large numerical ranges to make the data more interpretable.


4. Excel:

Purpose: Widely used tool for simple data analysis and visualization.

Best Practices:

Use pivot charts to summarize data interactively.

Stick to basic chart types (e.g., bar, line, pie) for easy-to-understand visuals.

Use conditional formatting to highlight key trends or outliers.

Label charts clearly (titles, axis names, and legends).

Limit the number of chart elements (don’t overcrowd your chart).


5. Google Data Studio:

Purpose: Free tool for creating dashboards and reports, often integrated with Google products.

Best Practices:

Link to live data sources for automatic updates (e.g., Google Sheets, Google Analytics).

Use dynamic filters to give users control over what data is shown.

Utilize templates for consistent reports and visuals.

Keep reports simple and focused on key metrics.

Design with mobile responsiveness in mind for accessibility.


6. Best Practices for Data Visualization:

Clarity over complexity: Simplify your visuals, removing unnecessary elements.

Choose the right chart: Select charts that best represent the data (e.g., bar for comparisons, line for trends).

Tell a story: Your visual should communicate a clear message or insight.

Consistency in design: Maintain a consistent style for fonts, colors, and layout across all visuals.

Be mindful of colorblindness: Use color schemes that are accessible to all viewers.

Provide context: Include clear titles, labels, and legends for better understanding.

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Hope it helps :)

NetApp hiring Machine Learning Engineer

https://careers.netapp.com/job/-/-/27600/79640994048?jobPipeline=limtedlistings

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Whether you’re a student, aspiring data analyst, software enthusiast, or just curious about AI, now’s the perfect time to dive in.

These 6 beginner-friendly and completely free AI courses from top institutions like Google, IBM, Harvard, and more

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3 ways to keep your data science skills up-to-date

1. Get Hands-On: Dive into real-world projects to grasp the challenges of building solutions. This is what will open up a world of opportunity for you to innovate.

2. Embrace the Big Picture: While deep diving into specific topics is essential, don't forget to understand the breadth of data science problem you are solving. Seeing the bigger picture helps you connect the dots and build solutions that not only are cutting edge but have a great ROI.

3. Network and Learn: Connect with fellow data scientists to exchange ideas, insights, and best practices. Learning from others in the field is invaluable for staying updated and continuously improving your skills.

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This FREE soft skills course is your gateway to mastering communication, teamwork, leadership, and more.

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Complete Roadmap to learn Machine Learning and Artificial Intelligence
👇👇

Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera

Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera

Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications

Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI

Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field

Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.

2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day.

Best Resources to learn ML & AI 👇

Learn Python for Free

Prompt Engineering Course

Prompt Engineering Guide

Data Science Course

Google Cloud Generative AI Path

Unlock the power of Generative AI Models

Machine Learning with Python Free Course

Machine Learning Free Book

Deep Learning Nanodegree Program with Real-world Projects

AI, Machine Learning and Deep Learning

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DATA SCIENCE JOBS ARE EXPLODING! 🤯💸

• Data Scientist: $118,399
• Data Analyst: $85,000
• Machine Learning Engineer: $123,117
• Business Intelligence Analyst: $97,000
• AI Researcher: $99,518

Top Ways Land a High-Paying Data Science Job:
1. Master Python & SQL
• Learn Pandas, NumPy, and Matplotlib.
• SQL is essential for handling databases.

2. Take Online Data Science Courses
• Platforms like Coursera, Udacity, and edX offer top courses.
• Certifications from Google or IBM add value.

3. Build a Strong Portfolio
• Work on real-world projects (Kaggle competitions, dashboards).
• Share projects on GitHub and LinkedIn.

4. Gain Experience with Internships & Freelance Work
• Apply for analyst roles or freelance on Upwork.
• Contribute to open-source projects.

5. Network & Stay Ahead
• Join data science meetups & LinkedIn groups.
• Follow industry leaders like Andrew Ng & Hadley Wickham.

Extra Tip: By Specializing in deep learning or NLP, you will stand out!

Data Science Jobs: 👇
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Google hiring Staff Data Scientist Product Manager, Data Science, Analytics

Apply link: https://careers.google.com/jobs/results/101440522295354054-staff-data-scientist-product-manager/

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We’re Hiring: Machine Learning Developer 🚀

At YASH Technologies, we’re building cutting-edge AI solutions across industries like commerce, finance, and agriculture. We’re looking for a Machine Learning Developer with expertise in LLMs, NLP, and Deep Learning to join our team!

What You’ll Do:

✅ Develop & optimize ML, NLP, and Generative AI models
✅ Fine-tune LLMs & work on prompt engineering
✅ Scale AI models from prototype to production
✅ Collaborate with global teams

What We’re Looking For:

🔹 2-6 years in ML, Deep Learning, NLP, and GenAI
🔹 Experience with TensorFlow, PyTorch, Hugging Face
🔹 Strong Python, R, or Scala skills
🔹 Knowledge of cloud-based ML platforms (AWS, Azure, GCP)

Interested? Let’s talk! Drop a comment or DM me at Poorva.bhatt@yash.com 📩

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Looking for Data Scientist professionals

Skills required - Proficient in R & Python with statistical modelling and ML techniques along with deep learning and NLP concepts.

Experience - 5-7 Years

Location - Bengaluru

Interested candidates can mail their cv at nidhig@symphonihr.com

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Sony Hiring Data Science Intern

Internship Duration: 6 months

Graduation Year: 2025 / 2026

Location: Bengaluru / Mumbai / Remote

Apply Link:
https://www.linkedin.com/jobs/view/4216336436/