Natwest hiring Data Scientist
Apply link: https://jobs.natwestgroup.com/jobs/16064413-data-scientist?tm_job=R-00256693-OTHLOC-IND-5FCHE051&tm_event=view&tm_company=861&bid=56
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Apply link: https://jobs.natwestgroup.com/jobs/16064413-data-scientist?tm_job=R-00256693-OTHLOC-IND-5FCHE051&tm_event=view&tm_company=861&bid=56
๐WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
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GE Aerospace is hiring!
Position: Data Scientist
Qualification: Bachelorโs/ Masterโs Degree
Salary: 8 - 16 LPA (Expected)
Experienc๏ปฟe: Entry Level
Location: Bengaluru, India
๐Apply Now: https://careers.geaerospace.com/global/en/job/R5005463/Data-Scientist
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Position: Data Scientist
Qualification: Bachelorโs/ Masterโs Degree
Salary: 8 - 16 LPA (Expected)
Experienc๏ปฟe: Entry Level
Location: Bengaluru, India
๐Apply Now: https://careers.geaerospace.com/global/en/job/R5005463/Data-Scientist
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1. Explain the concept of transfer learning in the context of deep learning models. How can it be beneficial in practical applications?
Ans- Transfer learning involves leveraging pre-trained models on large datasets and adapting them to new, related tasks with smaller datasets. In deep learning, this is achieved by reusing the knowledge gained during the training of one model on a different, but related, task. This is particularly beneficial when the new task has limited labeled data.
Practical applications include image recognition, where a model pre-trained on a dataset like ImageNet can be fine-tuned for a specific domain. Transfer learning accelerates model convergence, requires less labeled data, and helps overcome the challenges of training deep neural networks from scratch.
2. Given a large dataset, how would you efficiently sample a representative subset for model training? Discuss the trade-offs involved.
Answer- To efficiently sample a representative subset, one can use techniques like random sampling or stratified sampling. For random sampling, simple random sampling or systematic sampling methods can be employed. For stratified sampling, data is divided into strata, and samples are randomly selected from each stratum.
Trade-offs involve the choice between biased and unbiased sampling. Random sampling may not capture rare events, while stratified sampling might introduce complexity but ensures representation. The size of the sample is also crucial; a too-small sample may not be representative, while a too-large sample may incur unnecessary computational costs.
3. How would you approach analyzing A/B test results to determine the effectiveness of a new feature on a platform like Google Search?
Answer: A/B testing involves comparing the performance of two versions (A and B) to determine the impact of a change. To analyze A/B test results:
- Define Metrics: Clearly define key metrics (e.g., click-through rate, user engagement) before the test.
- Random Assignment: Ensure random assignment of users to control (A) and experimental (B) groups.
- Statistical Significance: Use statistical tests (e.g., t-test) to determine if differences between groups are statistically significant.
- Practical Significance: Consider the practical significance of results to assess real-world impact.
- Segmentation: Analyze results across different user segments for nuanced insights.
4. You have access to search query logs. How would you identify and address potential biases in the search results?
Answer: To identify and address biases in search results:
- Analyze Demographics: Examine user demographics to identify biases related to age, gender, or location.
- Query Intent: Understand user query intent and ensure diverse queries are well-represented.
- Evaluate Results: Assess the diversity of results to avoid favoring specific perspectives.
- User Feedback: Gather feedback from users to identify biased or inappropriate results.
- Continuous Monitoring: Implement continuous monitoring and iterate on algorithms to minimize biases.
Ans- Transfer learning involves leveraging pre-trained models on large datasets and adapting them to new, related tasks with smaller datasets. In deep learning, this is achieved by reusing the knowledge gained during the training of one model on a different, but related, task. This is particularly beneficial when the new task has limited labeled data.
Practical applications include image recognition, where a model pre-trained on a dataset like ImageNet can be fine-tuned for a specific domain. Transfer learning accelerates model convergence, requires less labeled data, and helps overcome the challenges of training deep neural networks from scratch.
2. Given a large dataset, how would you efficiently sample a representative subset for model training? Discuss the trade-offs involved.
Answer- To efficiently sample a representative subset, one can use techniques like random sampling or stratified sampling. For random sampling, simple random sampling or systematic sampling methods can be employed. For stratified sampling, data is divided into strata, and samples are randomly selected from each stratum.
Trade-offs involve the choice between biased and unbiased sampling. Random sampling may not capture rare events, while stratified sampling might introduce complexity but ensures representation. The size of the sample is also crucial; a too-small sample may not be representative, while a too-large sample may incur unnecessary computational costs.
3. How would you approach analyzing A/B test results to determine the effectiveness of a new feature on a platform like Google Search?
Answer: A/B testing involves comparing the performance of two versions (A and B) to determine the impact of a change. To analyze A/B test results:
- Define Metrics: Clearly define key metrics (e.g., click-through rate, user engagement) before the test.
- Random Assignment: Ensure random assignment of users to control (A) and experimental (B) groups.
- Statistical Significance: Use statistical tests (e.g., t-test) to determine if differences between groups are statistically significant.
- Practical Significance: Consider the practical significance of results to assess real-world impact.
- Segmentation: Analyze results across different user segments for nuanced insights.
4. You have access to search query logs. How would you identify and address potential biases in the search results?
Answer: To identify and address biases in search results:
- Analyze Demographics: Examine user demographics to identify biases related to age, gender, or location.
- Query Intent: Understand user query intent and ensure diverse queries are well-represented.
- Evaluate Results: Assess the diversity of results to avoid favoring specific perspectives.
- User Feedback: Gather feedback from users to identify biased or inappropriate results.
- Continuous Monitoring: Implement continuous monitoring and iterate on algorithms to minimize biases.
๐2
Pricelabs hiring Data Scientist
๐๐
https://hello.pricelabs.co/careers?jobId=UPL41kc6rPZK&ft_source=3000178039&ft_medium=3000170988
๐๐
https://hello.pricelabs.co/careers?jobId=UPL41kc6rPZK&ft_source=3000178039&ft_medium=3000170988
PriceLabs
Careers
Unlock Your Potential with Us Build Your Professional Journey at PriceLabs Join Us What We Offer You We care about your well-being and provide various benefits to support your professional journey and personal life. From flexible work arrangements to wellnessโฆ
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Key Concepts for Machine Learning Interviews
1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.
2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.
3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.
4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.
5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).
6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.
7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.
8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.
10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.
11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.
12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.
13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.
14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.
15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayesโ theorem, prior and posterior distributions, and Bayesian networks.
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1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.
2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.
3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.
4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.
5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).
6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.
7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.
8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.
10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.
11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.
12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.
13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.
14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.
15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayesโ theorem, prior and posterior distributions, and Bayesian networks.
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Ebay hiring Machine Learning Engineer
Apply link: https://jobs.ebayinc.com/us/en/job/EBAEBAUSR0067204EXTERNALENUS/Machine-Learning-Engineer-T24
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Apply link: https://jobs.ebayinc.com/us/en/job/EBAEBAUSR0067204EXTERNALENUS/Machine-Learning-Engineer-T24
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Join us in building and scaling the infrastructure powering the future of Generative AI. We're looking for passionate individuals to help drive impactful projects.
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๐๐ข๐ง๐ค๐:-
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Start Learning Today!โ ๏ธ
๐ ๐๐๐ฒ๐ฌ ๐ญ๐จ ๐๐ฉ๐ฉ๐ฅ๐ฒ ๐๐จ๐ซ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ ๐๐จ๐๐ฌ
๐ธ๐๐ฌ๐ ๐๐จ๐ ๐๐จ๐ซ๐ญ๐๐ฅ๐ฌ
Job boards like LinkedIn & Naukari are great portals to find jobs.
Set up job alerts using keywords like โData Analystโ so youโll get notified as soon as something new comes up.
๐ธ๐๐๐ข๐ฅ๐จ๐ซ ๐๐จ๐ฎ๐ซ ๐๐๐ฌ๐ฎ๐ฆ๐
Donโt send the same resume to every job.
Take time to highlight the skills and tools that the job description asks for, like SQL, Power BI, or Excel. It helps your resume get noticed by software that scans for keywords (ATS).
๐ธ๐๐ฌ๐ ๐๐ข๐ง๐ค๐๐๐๐ง
Connect with recruiters and employees from your target companies. Ask for referrals when any jib opening is poster
Engage with data-related content and share your own work (like project insights or dashboards).
๐ธ๐๐ก๐๐๐ค ๐๐จ๐ฆ๐ฉ๐๐ง๐ฒ ๐๐๐๐ฌ๐ข๐ญ๐๐ฌ ๐๐๐ ๐ฎ๐ฅ๐๐ซ๐ฅ๐ฒ
Most big companies post jobs directly on their websites first.
Create a list of companies youโre interested in and keep checking their careers page. Itโs a good way to find openings early before they post on job portals.
๐ธ๐ ๐จ๐ฅ๐ฅ๐จ๐ฐ ๐๐ฉ ๐๐๐ญ๐๐ซ ๐๐ฉ๐ฉ๐ฅ๐ฒ๐ข๐ง๐
After applying to a job, it helps to follow up with a quick message on LinkedIn. You can send a polite note to recruiter and aks for the update on your candidature.
๐ธ๐๐ฌ๐ ๐๐จ๐ ๐๐จ๐ซ๐ญ๐๐ฅ๐ฌ
Job boards like LinkedIn & Naukari are great portals to find jobs.
Set up job alerts using keywords like โData Analystโ so youโll get notified as soon as something new comes up.
๐ธ๐๐๐ข๐ฅ๐จ๐ซ ๐๐จ๐ฎ๐ซ ๐๐๐ฌ๐ฎ๐ฆ๐
Donโt send the same resume to every job.
Take time to highlight the skills and tools that the job description asks for, like SQL, Power BI, or Excel. It helps your resume get noticed by software that scans for keywords (ATS).
๐ธ๐๐ฌ๐ ๐๐ข๐ง๐ค๐๐๐๐ง
Connect with recruiters and employees from your target companies. Ask for referrals when any jib opening is poster
Engage with data-related content and share your own work (like project insights or dashboards).
๐ธ๐๐ก๐๐๐ค ๐๐จ๐ฆ๐ฉ๐๐ง๐ฒ ๐๐๐๐ฌ๐ข๐ญ๐๐ฌ ๐๐๐ ๐ฎ๐ฅ๐๐ซ๐ฅ๐ฒ
Most big companies post jobs directly on their websites first.
Create a list of companies youโre interested in and keep checking their careers page. Itโs a good way to find openings early before they post on job portals.
๐ธ๐ ๐จ๐ฅ๐ฅ๐จ๐ฐ ๐๐ฉ ๐๐๐ญ๐๐ซ ๐๐ฉ๐ฉ๐ฅ๐ฒ๐ข๐ง๐
After applying to a job, it helps to follow up with a quick message on LinkedIn. You can send a polite note to recruiter and aks for the update on your candidature.
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