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Job Role: Graduate Trainee
Qualifications: Non-engineering Bachelorโs Degree
Salary: Up to 16 LPA (Expected)
Batch: 2024/ 2025/ 2026
Experience: Freshers
Location: Bengaluru; Hyderabad, India
๐Apply Now: https://www.goldmansachs.com/careers/students/programs/india/graduate-campus-hiring-program.html
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Practical Scenario-Based Machine Learning Interview Questions for Entry-Level Machine Learning Engineers | Mandar Patil postedโฆ
Practical Scenario-Based Machine Learning Interview Questions for Entry-Level Machine Learning Engineers
Preparing for a machine learning engineer role? These practical, scenario-based interview questions will help you demonstrate your real-world problemโฆ
Preparing for a machine learning engineer role? These practical, scenario-based interview questions will help you demonstrate your real-world problemโฆ
๐2
Machine Learning Interview Questions.pdf
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Machine Learning Interview Questions๐ฅ๐ฅ๐ฅ
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Machine Learning Interview Questions for Entry-Level Machine Learning Engineers
1. What is Machine Learning?
Explain the basic concept and types of machine learning (supervised, unsupervised, and reinforcement learning).
2. Difference between AI, Machine Learning, and Deep Learning?
Define each term and describe how they are related to each other.
3. What is Overfitting and Underfitting?
Describe the concepts and how to address them.
4. Explain the Bias-Variance Tradeoff.
Discuss the tradeoff and its significance in model performance.
5. What is Cross-Validation?
Explain different cross-validation techniques and their importance.
6. Describe the process of Feature Engineering.
Discuss techniques used to create and select features for a model.
7. What are Confusion Matrix and its components?
Define and explain precision, recall, F1 score, and accuracy.
8. Explain Regularization and its types.
Discuss L1 (Lasso) and L2 (Ridge) regularization.
9. What is Gradient Descent?
Describe the algorithm and its variants (batch, stochastic, mini-batch).
10. Difference between Classification and Regression?
Provide examples of each type of problem.
11. Explain Principal Component Analysis (PCA).
Discuss how PCA is used for dimensionality reduction.
12. What is a Support Vector Machine (SVM)?
Describe the algorithm and its applications.
13. Explain K-Nearest Neighbors (KNN).
Discuss how KNN works and its pros and cons.
14. Describe Decision Trees and Random Forests.
Explain the algorithms and how Random Forests improve performance.
15. What is a Neural Network?
Define the basic components (neurons, layers, activation functions).
16. Difference between Bagging and Boosting?
Describe each technique and their use cases.
17. Explain Hyperparameter Tuning.
Discuss methods such as grid search and random search.
18. What are Ensemble Methods?
Explain how ensemble methods like stacking and voting improve model performance.
19. What is a ROC Curve?
Describe how it is used to evaluate model performance.
20. Explain the concept of Clustering.
Discuss different clustering algorithms (K-means, hierarchical, DBSCAN).
21. What are Recommender Systems?
Explain the types and how they work.
22. How do you handle Missing Data?
Describe different strategies for dealing with missing values.
23. What is a Time Series Forecasting?
Explain how time series data is different and the techniques used for forecasting.
24. What are Autoencoders?
Describe their architecture and use cases.
25. Explain the term 'Feature Scaling'.
Discuss different methods of scaling features.
26. What is a Confusion Matrix?
Describe its importance in evaluating classification models.
27. Difference between Parametric and Non-Parametric Models?
Define each and provide examples.
28. What are ROC and AUC?
Explain how they are used to measure model performance.
29. Describe Transfer Learning.
Explain how pre-trained models are used in transfer learning.
30. What is an Outlier?
Discuss how to detect and handle outliers in a dataset.
1. What is Machine Learning?
Explain the basic concept and types of machine learning (supervised, unsupervised, and reinforcement learning).
2. Difference between AI, Machine Learning, and Deep Learning?
Define each term and describe how they are related to each other.
3. What is Overfitting and Underfitting?
Describe the concepts and how to address them.
4. Explain the Bias-Variance Tradeoff.
Discuss the tradeoff and its significance in model performance.
5. What is Cross-Validation?
Explain different cross-validation techniques and their importance.
6. Describe the process of Feature Engineering.
Discuss techniques used to create and select features for a model.
7. What are Confusion Matrix and its components?
Define and explain precision, recall, F1 score, and accuracy.
8. Explain Regularization and its types.
Discuss L1 (Lasso) and L2 (Ridge) regularization.
9. What is Gradient Descent?
Describe the algorithm and its variants (batch, stochastic, mini-batch).
10. Difference between Classification and Regression?
Provide examples of each type of problem.
11. Explain Principal Component Analysis (PCA).
Discuss how PCA is used for dimensionality reduction.
12. What is a Support Vector Machine (SVM)?
Describe the algorithm and its applications.
13. Explain K-Nearest Neighbors (KNN).
Discuss how KNN works and its pros and cons.
14. Describe Decision Trees and Random Forests.
Explain the algorithms and how Random Forests improve performance.
15. What is a Neural Network?
Define the basic components (neurons, layers, activation functions).
16. Difference between Bagging and Boosting?
Describe each technique and their use cases.
17. Explain Hyperparameter Tuning.
Discuss methods such as grid search and random search.
18. What are Ensemble Methods?
Explain how ensemble methods like stacking and voting improve model performance.
19. What is a ROC Curve?
Describe how it is used to evaluate model performance.
20. Explain the concept of Clustering.
Discuss different clustering algorithms (K-means, hierarchical, DBSCAN).
21. What are Recommender Systems?
Explain the types and how they work.
22. How do you handle Missing Data?
Describe different strategies for dealing with missing values.
23. What is a Time Series Forecasting?
Explain how time series data is different and the techniques used for forecasting.
24. What are Autoencoders?
Describe their architecture and use cases.
25. Explain the term 'Feature Scaling'.
Discuss different methods of scaling features.
26. What is a Confusion Matrix?
Describe its importance in evaluating classification models.
27. Difference between Parametric and Non-Parametric Models?
Define each and provide examples.
28. What are ROC and AUC?
Explain how they are used to measure model performance.
29. Describe Transfer Learning.
Explain how pre-trained models are used in transfer learning.
30. What is an Outlier?
Discuss how to detect and handle outliers in a dataset.
๐3โค2
Practical Scenario-Based Machine Learning Interview Questions for Entry-Level Machine Learning Engineers
1. Scenario: Customer Churn Prediction
How would you approach building a model to predict customer churn for a subscription-based service?
What features would you consider, and how would you handle imbalanced classes?
2. Scenario: Recommendation System
Describe how you would design a recommendation system for an e-commerce platform.
What algorithms might you use, and how would you evaluate the performance of your recommendation system?
3. Scenario: Fraud Detection
How would you build a model to detect fraudulent transactions in a financial dataset?
What techniques would you use to handle class imbalance and feature selection?
4. Scenario: Image Classification
Explain how you would create a model to classify images into different categories.
What pre-processing steps would you take, and how would you handle overfitting in your model?
5. Scenario: Sentiment Analysis
How would you build a sentiment analysis model for customer reviews?
Describe the process of text preprocessing and the choice of algorithms for this task.
6. Scenario: Predictive Maintenance
Discuss how you would develop a predictive maintenance model for industrial machinery.
What types of data would you collect, and how would you implement and evaluate your model?
7. Scenario: Time Series Forecasting
How would you approach forecasting sales data for the next quarter?
What models are suitable for time series forecasting, and how would you handle seasonality and trends?
8. Scenario: Anomaly Detection
Describe how you would build an anomaly detection system for network security.
What techniques would you use, and how would you evaluate the effectiveness of your system?
9. Scenario: Natural Language Processing
How would you build a chatbot that can handle customer service inquiries?
What NLP techniques would you use, and how would you ensure the chatbot understands and responds accurately?
10. Scenario: Feature Engineering
Given a dataset with various features, how would you approach feature engineering to improve model performance?
Provide examples of techniques you would use for feature creation and selection.
11. Scenario: Model Deployment
After training a machine learning model, how would you deploy it into a production environment?
What steps would you take to monitor and maintain the modelโs performance?
12. Scenario: Model Interpretability
How would you ensure that your machine learning model is interpretable and explainable to stakeholders?
Discuss techniques and tools you would use for model interpretability.
13. Scenario: Hyperparameter Tuning
Explain how you would perform hyperparameter tuning for a machine learning model.
What methods would you use, and how would you validate the results?
14. Scenario: Handling Missing Data
In a dataset with missing values, how would you handle the missing data before building a model?
Discuss different strategies and their impact on model performance.
15. Scenario: Model Evaluation
How would you evaluate the performance of a machine learning model in a real-world scenario?
Discuss different evaluation metrics and their relevance to specific problems.
1. Scenario: Customer Churn Prediction
How would you approach building a model to predict customer churn for a subscription-based service?
What features would you consider, and how would you handle imbalanced classes?
2. Scenario: Recommendation System
Describe how you would design a recommendation system for an e-commerce platform.
What algorithms might you use, and how would you evaluate the performance of your recommendation system?
3. Scenario: Fraud Detection
How would you build a model to detect fraudulent transactions in a financial dataset?
What techniques would you use to handle class imbalance and feature selection?
4. Scenario: Image Classification
Explain how you would create a model to classify images into different categories.
What pre-processing steps would you take, and how would you handle overfitting in your model?
5. Scenario: Sentiment Analysis
How would you build a sentiment analysis model for customer reviews?
Describe the process of text preprocessing and the choice of algorithms for this task.
6. Scenario: Predictive Maintenance
Discuss how you would develop a predictive maintenance model for industrial machinery.
What types of data would you collect, and how would you implement and evaluate your model?
7. Scenario: Time Series Forecasting
How would you approach forecasting sales data for the next quarter?
What models are suitable for time series forecasting, and how would you handle seasonality and trends?
8. Scenario: Anomaly Detection
Describe how you would build an anomaly detection system for network security.
What techniques would you use, and how would you evaluate the effectiveness of your system?
9. Scenario: Natural Language Processing
How would you build a chatbot that can handle customer service inquiries?
What NLP techniques would you use, and how would you ensure the chatbot understands and responds accurately?
10. Scenario: Feature Engineering
Given a dataset with various features, how would you approach feature engineering to improve model performance?
Provide examples of techniques you would use for feature creation and selection.
11. Scenario: Model Deployment
After training a machine learning model, how would you deploy it into a production environment?
What steps would you take to monitor and maintain the modelโs performance?
12. Scenario: Model Interpretability
How would you ensure that your machine learning model is interpretable and explainable to stakeholders?
Discuss techniques and tools you would use for model interpretability.
13. Scenario: Hyperparameter Tuning
Explain how you would perform hyperparameter tuning for a machine learning model.
What methods would you use, and how would you validate the results?
14. Scenario: Handling Missing Data
In a dataset with missing values, how would you handle the missing data before building a model?
Discuss different strategies and their impact on model performance.
15. Scenario: Model Evaluation
How would you evaluate the performance of a machine learning model in a real-world scenario?
Discuss different evaluation metrics and their relevance to specific problems.
๐4
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Hey hustlers, I hope you're doing great.
Welcome Day 2 of the ๐๐๐๐๐น๐ฒ๐ฟ๐๐น๐ฒ๐ฟ๐ ๐๐ผ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ๐๐ฝ๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐๐ผ ๐๐ผ๐ฟ๐ธ๐ถ๐ป๐ด ๐ฎ๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ challenge.
Today we have to learn the mathematics and statistics part, which is very essential because these two are used in Python, SQL, and Excel too, so we need to have a solid grip on these fundamentals of data analytics.
Here I'm sharing a few YouTube tutorials and resources so that you can go through them and complete today's challenge by the end of the day and don't forget to work on your health which is another essential part of this challenge
https://www.youtube.com/watch?v=o7WkGYIPUM8&list=PLunlGNVWDAaY7AeDDzTeu4-DD3g7zmAXs
W3School Statistics Tutorial:https://www.w3schools.com/statistics/
Looking forward to your responses.
Welcome Day 2 of the ๐๐๐๐๐น๐ฒ๐ฟ๐๐น๐ฒ๐ฟ๐ ๐๐ผ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ๐๐ฝ๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐๐ผ ๐๐ผ๐ฟ๐ธ๐ถ๐ป๐ด ๐ฎ๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ challenge.
Today we have to learn the mathematics and statistics part, which is very essential because these two are used in Python, SQL, and Excel too, so we need to have a solid grip on these fundamentals of data analytics.
Here I'm sharing a few YouTube tutorials and resources so that you can go through them and complete today's challenge by the end of the day and don't forget to work on your health which is another essential part of this challenge
https://www.youtube.com/watch?v=o7WkGYIPUM8&list=PLunlGNVWDAaY7AeDDzTeu4-DD3g7zmAXs
W3School Statistics Tutorial:https://www.w3schools.com/statistics/
Looking forward to your responses.
YouTube
Statistics For Data Analytics | Complete Syllabus | Data Science | Statistics Tutorial | Part 1
In this video, we present the complete syllabus for our "Statistics for Data Analytics" course. Whether you're a data enthusiast, student, or professional looking to sharpen your statistical skills, this syllabus overview will give you a sneak peek into whatโฆ
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PrepNPlaced pinned ยซHey hustlers, I hope you're doing great. Welcome Day 2 of the ๐๐๐๐๐น๐ฒ๐ฟ๐๐น๐ฒ๐ฟ๐ ๐๐ผ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ๐๐ฝ๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐๐ผ ๐๐ผ๐ฟ๐ธ๐ถ๐ป๐ด ๐ฎ๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ challenge. Today we have to learn the mathematics and statistics part, which is very essential because these two are usedโฆยป
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๐ช๐ต๐ ๐ผ๐๐ฒ๐ฟ๐ฐ๐ผ๐บ๐ถ๐ป๐ด ๐ผ๐๐ฟ ๐ณ๐ฒ๐ฎ๐ฟ๐ ๐ถ๐ ๐ต๐ฒ๐น๐ฝ๐ณ๐๐น ๐ณ๐ผ๐ฟ ๐ผ๐๐ฟ ๐ผ๐๐ป ๐ด๐ฟ๐ผ๐๐๐ต ๐ฎ๐ป๐ฑ ๐ฑ๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐๐ฅ๐ฅ๐ฅ
https://www.linkedin.com/posts/mandarmpatil_fearlessliving-embracethejourney-yougotthis-activity-7215542589314220033-lLQh
https://www.linkedin.com/posts/mandarmpatil_fearlessliving-embracethejourney-yougotthis-activity-7215542589314220033-lLQh
Linkedin
Mandar Patil on LinkedIn: #fearlessliving #embracethejourney #yougotthis #overcomingfearโฆ | 77 comments
Are Your Fears Sabotaging Your Success? Here's How to Turn Them Around! ๐
We all dream of being fearless. Imagine yourself boldly pursuing your passionsโฆ | 77 comments on LinkedIn
We all dream of being fearless. Imagine yourself boldly pursuing your passionsโฆ | 77 comments on LinkedIn
โค2
Cognizant is hiring
Position: Junior Data Analyst
Experienc๏ปฟe: Freshers
Location: Mumbai; Kolkata; Pune, India (Hybrid)
https://careers.cognizant.com/global-en/jobs/00059475331/trainee-junior-data-analyst/?src=SNS-102?JB-11500 (MUMBAI)
https://careers.cognizant.com/global-en/jobs/00059570581/trainee-junior-data-analyst/?src=SNS-102?JB-11500 (KOLKATA)
https://careers.cognizant.com/global-en/jobs/00059444541/junior-data-analyst/?src=SNS-102?JB-11500 (PUNE)
๐WhatsApp Channel: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46
๐Telegram Link: https://t.me/dataanalyticsbuddy
Like for more โค๏ธ
All the best ๐๐
Position: Junior Data Analyst
Experienc๏ปฟe: Freshers
Location: Mumbai; Kolkata; Pune, India (Hybrid)
https://careers.cognizant.com/global-en/jobs/00059475331/trainee-junior-data-analyst/?src=SNS-102?JB-11500 (MUMBAI)
https://careers.cognizant.com/global-en/jobs/00059570581/trainee-junior-data-analyst/?src=SNS-102?JB-11500 (KOLKATA)
https://careers.cognizant.com/global-en/jobs/00059444541/junior-data-analyst/?src=SNS-102?JB-11500 (PUNE)
๐WhatsApp Channel: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46
๐Telegram Link: https://t.me/dataanalyticsbuddy
Like for more โค๏ธ
All the best ๐๐
๐3โค2
Company Name : Solidus AI
Challenge Name : Solidus AI Tech Hack-AI-Thon
Eligibility : College Students from first to final year + Working Professionals.
Prizes worth 16 Lakhs and also a chance to earn from solution you build.
Link to register : https://solidus.hackerearth.com/
Challenge Name : Solidus AI Tech Hack-AI-Thon
Eligibility : College Students from first to final year + Working Professionals.
Prizes worth 16 Lakhs and also a chance to earn from solution you build.
Link to register : https://solidus.hackerearth.com/
HackerEarth
Solidus Ai Tech Hack-AI-Thon on HackerEarth
About the Challenge
"Shape the Future of AI: Compete, Create, Conquer!" Join us in an exhilarating challenge to push the boundaries of artificial intelligence. This hackathon is designed for innovative minds ready to develop groundbreaking AI solutions andโฆ
"Shape the Future of AI: Compete, Create, Conquer!" Join us in an exhilarating challenge to push the boundaries of artificial intelligence. This hackathon is designed for innovative minds ready to develop groundbreaking AI solutions andโฆ
โค2๐1
Hey hustlers, I hope you're doing great.
Welcome Day 3 of the ๐๐๐๐๐น๐ฒ๐ฟ๐๐น๐ฒ๐ฟ๐ ๐๐ผ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ๐๐ฝ๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐๐ผ ๐๐ผ๐ฟ๐ธ๐ถ๐ป๐ด ๐ฎ๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ challenge.
Today is Sunday, which means resting the whole day, right??
โ Yes ๐
โ Nope
Remember who we are. We are hustlers who don't give excuses, and our aim is much bigger. For that, we break all tasks into smaller tasks and try to achieve them all at once.
Don;t forget on these important part of this challenge
1. Taking enough amount sleep of 6โ8 hours daily
2. Eating only healthy and home-made food and trying to avoid junk food as much as possible to eliminate sugary and packaged food
3. Daily 30 minutes of exercise. Here, you don't need to join any gym; you can do just walking and some yoga to make us stronger.
4. Drinking 3โ4 litres of water
5. At least 2 minutes of deep breathing will make us calm and focus on only positive things. This will eliminate all your anxiety and negativity.
6. Try to give a minimum 1 hour for learning new skills, which can be helpful for getting a better job or making money.
So today's task is to understand more advanced concepts in math and statistics. Here are more resources for your learning.
https://www.youtube.com/playlist?list=PLunlGNVWDAaY7AeDDzTeu4-DD3g7zmAXs
Looking forward to your support and responses.
Welcome Day 3 of the ๐๐๐๐๐น๐ฒ๐ฟ๐๐น๐ฒ๐ฟ๐ ๐๐ผ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ๐๐ฝ๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐๐ผ ๐๐ผ๐ฟ๐ธ๐ถ๐ป๐ด ๐ฎ๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ challenge.
Today is Sunday, which means resting the whole day, right??
โ Yes ๐
โ Nope
Remember who we are. We are hustlers who don't give excuses, and our aim is much bigger. For that, we break all tasks into smaller tasks and try to achieve them all at once.
Don;t forget on these important part of this challenge
1. Taking enough amount sleep of 6โ8 hours daily
2. Eating only healthy and home-made food and trying to avoid junk food as much as possible to eliminate sugary and packaged food
3. Daily 30 minutes of exercise. Here, you don't need to join any gym; you can do just walking and some yoga to make us stronger.
4. Drinking 3โ4 litres of water
5. At least 2 minutes of deep breathing will make us calm and focus on only positive things. This will eliminate all your anxiety and negativity.
6. Try to give a minimum 1 hour for learning new skills, which can be helpful for getting a better job or making money.
So today's task is to understand more advanced concepts in math and statistics. Here are more resources for your learning.
https://www.youtube.com/playlist?list=PLunlGNVWDAaY7AeDDzTeu4-DD3g7zmAXs
Looking forward to your support and responses.
๐12โค2
PrepNPlaced pinned ยซHey hustlers, I hope you're doing great. Welcome Day 3 of the ๐๐๐๐๐น๐ฒ๐ฟ๐๐น๐ฒ๐ฟ๐ ๐๐ผ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ๐๐ฝ๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐๐ผ ๐๐ผ๐ฟ๐ธ๐ถ๐ป๐ด ๐ฎ๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ challenge. Today is Sunday, which means resting the whole day, right?? โ
Yes ๐ โ Nope Remember who we are. We are hustlersโฆยป
Data Science Job Updates
Company โ IIDE Education Private Limited
Role โ Data Analyst Internship
Exp. โ Fresher
Apply Here โ https://internshala.com/internship/detail/data-analyst-internship-in-mumbai-at-iide-education-private-limited1720264879
Company โ Blackcoffer
Role โ Data Science Internship
Exp. โ Fresher
Apply Here โ https://internshala.com/internship/details/work-from-home-data-science-internship-at-blackcoffer1720199867
Company โ AskmeOffers.com
Role โ Data Analyst
Exp. โ Fresher
Apply Here โ https://www.linkedin.com/jobs/view/3965852124
Company โ White Gold Bullion Private Limited
Role โ Mis Executive
Exp. โ 0-3 yrs
Apply Here โ https://www.foundit.in/job/mis-executive-white-gold-bullion-private-limited-bengaluru-bangalore-31074671
Company โ Blockchain for the Next Billion
Role โ Data Analyst
Exp. โ 0-5 yrs
Apply Here โ https://www.linkedin.com/jobs/view/3966385446
WhatsApp Channel: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46
Company โ Triconn Solutions
Role โ Business Analyst
Exp. โ 1yr
Apply Here โ https://www.simplyhired.co.in/job/GMwVaQjIUWtLTnoj6i6CcKeYSAvpUeVOh9FvBVOqcLlXVyhWJk8bjg
Company โ Altimetrik
Role โ Data Engineer
Exp. โ 1+ yr
Apply Here โ https://www.linkedin.com/jobs/view/3966396127
Company โ Comcast Corporation
Role โ Data Science Engineer 2
Exp. โ 2-5 yrs
Apply Here โ https://www.simplyhired.co.in/job/P9lk0zbGaOaj_LkkUDWh4xVXvR_cIJOMtPgB3Mk2aNepKT85N9z-rw
Telegram Link: https://t.me/dataanalyticsbuddy
Company โ Innspark Solutions Pvt. Ltd.
Role โ AI Engineer
Exp. โ 2 yrs
Apply Here โ https://www.simplyhired.co.in/job/4xbpP0eBoD1vCNW7Z3EQwPVZ7pMbjwxC9WYiccq3I1kCkHNoCs5BBw
Company โ Microsoft
Role โ Operational Data Scientist
Exp. โ 2-3 yrs
Apply Here โ https://www.simplyhired.co.in/job/y8Gw2LXWQWb7LzwDbFQB11e7KqJ3js_6cPxsSlC5F_e11QS4nNJGag
Company โ IIDE Education Private Limited
Role โ Data Analyst Internship
Exp. โ Fresher
Apply Here โ https://internshala.com/internship/detail/data-analyst-internship-in-mumbai-at-iide-education-private-limited1720264879
Company โ Blackcoffer
Role โ Data Science Internship
Exp. โ Fresher
Apply Here โ https://internshala.com/internship/details/work-from-home-data-science-internship-at-blackcoffer1720199867
Company โ AskmeOffers.com
Role โ Data Analyst
Exp. โ Fresher
Apply Here โ https://www.linkedin.com/jobs/view/3965852124
Company โ White Gold Bullion Private Limited
Role โ Mis Executive
Exp. โ 0-3 yrs
Apply Here โ https://www.foundit.in/job/mis-executive-white-gold-bullion-private-limited-bengaluru-bangalore-31074671
Company โ Blockchain for the Next Billion
Role โ Data Analyst
Exp. โ 0-5 yrs
Apply Here โ https://www.linkedin.com/jobs/view/3966385446
WhatsApp Channel: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46
Company โ Triconn Solutions
Role โ Business Analyst
Exp. โ 1yr
Apply Here โ https://www.simplyhired.co.in/job/GMwVaQjIUWtLTnoj6i6CcKeYSAvpUeVOh9FvBVOqcLlXVyhWJk8bjg
Company โ Altimetrik
Role โ Data Engineer
Exp. โ 1+ yr
Apply Here โ https://www.linkedin.com/jobs/view/3966396127
Company โ Comcast Corporation
Role โ Data Science Engineer 2
Exp. โ 2-5 yrs
Apply Here โ https://www.simplyhired.co.in/job/P9lk0zbGaOaj_LkkUDWh4xVXvR_cIJOMtPgB3Mk2aNepKT85N9z-rw
Telegram Link: https://t.me/dataanalyticsbuddy
Company โ Innspark Solutions Pvt. Ltd.
Role โ AI Engineer
Exp. โ 2 yrs
Apply Here โ https://www.simplyhired.co.in/job/4xbpP0eBoD1vCNW7Z3EQwPVZ7pMbjwxC9WYiccq3I1kCkHNoCs5BBw
Company โ Microsoft
Role โ Operational Data Scientist
Exp. โ 2-3 yrs
Apply Here โ https://www.simplyhired.co.in/job/y8Gw2LXWQWb7LzwDbFQB11e7KqJ3js_6cPxsSlC5F_e11QS4nNJGag
Internshala
Data Analyst Internship in Mumbai at IIDE Education
Selected intern's day-to-day responsibilities include:
1. Integrate new technologies into the business.
2. Report using Google Data Studio, Google Sheets, and Excel.
3. Monitor and manage our learning management system.
4. Utilize software suchโฆ
1. Integrate new technologies into the business.
2. Report using Google Data Studio, Google Sheets, and Excel.
3. Monitor and manage our learning management system.
4. Utilize software suchโฆ
๐5
Wipro is hiring!
Position: Data Engineer
Qualifications: Bachelorโs/ Masterโs Degree
Salary: 5 - 11 LPA (Expected)
Experience: Freshers (0 - 2 Years)
Location: Bengaluru; Hyderabad; Pune, India
๐Apply Now: https://careers.wipro.com/careers-home/jobs/3071022
Position: Data Engineer
Qualifications: Bachelorโs/ Masterโs Degree
Salary: 5 - 11 LPA (Expected)
Experience: Freshers (0 - 2 Years)
Location: Bengaluru; Hyderabad; Pune, India
๐Apply Now: https://careers.wipro.com/careers-home/jobs/3071022
Wipro
Wipro Careers | Reinvent Your World
Explore diverse and global projects at Wipro. Seize opportunities for growth, learning, and development. Join us to shape the future with confidence and innovation.
United Airlines is hiring for Associate Data Engineer
Expected Salary: 8-12 LPA
Apply here:
https://careers.united.com/us/en/job/UAIUADUSGGN00001543EXTERNALENUSTALEO/Associate-Data-Engineer
Expected Salary: 8-12 LPA
Apply here:
https://careers.united.com/us/en/job/UAIUADUSGGN00001543EXTERNALENUSTALEO/Associate-Data-Engineer