𝐙𝐞𝐨𝐭𝐚𝐩 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐈𝐧𝐭𝐞𝐫𝐧𝐬𝐡𝐢𝐩, 𝟐𝟎𝟐𝟓!
Position: Data Science - Intern
Qualifications: Bachelor’s/ Master’s Degree
Salary: ₹35,000 Per Month (Expected)
Batch: 2025/ 2026/ 2027
Experience: Freshers
Location: Bengaluru, India

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Company Name: Airbus
Role: Machine Learning Intern
Batch Eligible: 2025 & 2026 passouts
Location: Bengaluru

Apply Link: https://ag.wd3.myworkdayjobs.com/en-US/Airbus/job/Machine-Learning-Intern_JR10306173

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Company Name: Sony
Role: Data Science Intern
Batch Eligible: Being remote job all college students can try who have desired skills
Location: Remote

Apply Link: https://www.linkedin.com/jobs/view/4127149988/

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Hiring: Data Annotators! 📊


Are you passionate about data and eager to contribute to AI projects? Join our team and help build high-quality datasets that power machine learning models.

📧 Send your resume to: ankur.vatsal@deepmatrix.io

What You’ll Do:
Annotate text and image data
Ensure accuracy and consistency
Collaborate to improve annotation processes
Play a key role in AI model performance

Who You Are:
Detail-oriented with a focus on quality
Familiar with data annotation techniques (experience with tools a plus)
Independent, deadline-driven, and passionate about AI

Apple is hiring Data Scientist
https://jobs.apple.com/en-us/details/200578119/ml-data-scientist

Projects to Master Python
👇👇
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Real is hiring AI Engineer
Experience: Freshers
Location: India (Remote)
https://jobs.ashbyhq.com/real/70c2b22b-0a82-4f54-a673-a6d5fb598de3

Senstosoft Looking for Senior Power BI Developer

Experience: 8-11 Years only

https://www.linkedin.com/posts/potanapalli-rupa-abb816152_powerbi-sql-dax-activity-7288461445137059840-MLcC

Hiring : Computer Vision Engineer 🌐

💻 Location: Remote (US, India, Europe)
Join a cutting-edge startup revolutionizing the manufacturing industry with deep tech! We're on the lookout for a Computer Vision Engineer to design and deploy advanced vision systems for real-time object detection, defect identification, and robotic automation.

🔑 Key Skills:
Expertise in object detection, tracking, and 3D vision pipelines
Proficiency in Python/C++, OpenCV, PyTorch, TensorFlow
Experience with real-time systems and edge devices like NVIDIA Jetson
https://docs.google.com/forms/d/e/1FAIpQLSds229TBuj5OuJBKcA0eKH5QbHlln8ebHbjvglaE2Ks-G09kg/viewform

Exciting Opportunities Await!!!!

We are Seeking s seasoned Data Scientist Professional who is specializing in
SQL/Python, Machine Learning, Deep Learning, GenAI
Experience Required: 2 to 5 Years
At least 1.6 Years of Experience in GenAI (LLM+RAG)
Relevant Experience in Data Science should be 2+ Years

We look forward to hearing from talented professionals eager to make an impact!
If you're available to join within 30 days, please send your updated resume to shruthik@novotreeminds.com

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

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.

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 😄👍

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/

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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

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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

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.