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Applied Materials Data Scientist Interview Questions and Answers

Updated 18 Aug 2024

Applied Materials Data Scientist Interview Experiences

3 interviews found

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
6-8 weeks
Result
Selected Selected

I applied via LinkedIn and was interviewed in Jul 2024. There were 2 interview rounds.

Round 1 - One-on-one 

(2 Questions)

  • Q1. Explain about the project
  • Ans. 

    Developed a machine learning model to predict customer churn for a telecom company

    • Used historical customer data to train the model

    • Implemented various classification algorithms such as logistic regression, random forest, and XGBoost

    • Evaluated model performance using metrics like accuracy, precision, recall, and F1 score

  • Answered by AI
  • Q2. Basic SQLQuestions
Round 2 - Panel interview 

(2 Questions)

  • Q1. Intermediate level SQL questions
  • Q2. Intermediate level python/machine learning questions
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Referral and was interviewed before Aug 2023. There were 2 interview rounds.

Round 1 - Technical 

(2 Questions)

  • Q1. Program complexity.
  • Q2. Code efficiency
Round 2 - HR 

(2 Questions)

  • Q1. What do you like about AMAT?
  • Ans. 

    AMAT is a leading provider of semiconductor manufacturing equipment and services.

    • AMAT is known for its cutting-edge technology and innovation in the semiconductor industry.

    • I appreciate AMAT's commitment to research and development, constantly pushing the boundaries of what is possible.

    • The company has a strong global presence and a track record of delivering high-quality products and services.

    • AMAT's focus on sustainabil...

  • Answered by AI
  • Q2. How do you like working?
  • Ans. 

    I enjoy working in a collaborative environment where I can use my analytical skills to solve complex problems.

    • I thrive in environments where I can work with a team to brainstorm ideas and solutions.

    • I appreciate opportunities to use data analysis techniques to uncover insights and drive decision-making.

    • I value a work culture that encourages continuous learning and professional growth.

    • I find satisfaction in overcoming ch...

  • Answered by AI

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Interview experience
3
Average
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Referral and was interviewed before May 2023. There were 3 interview rounds.

Round 1 - Technical 

(1 Question)

  • Q1. How Decision tree works
  • Ans. 

    Decision tree is a predictive modeling tool that uses a tree-like graph of decisions and their possible consequences.

    • Decision tree splits data into subsets based on the value of a certain attribute

    • It recursively divides data into smaller subsets until a stopping criterion is met

    • Each internal node represents a decision based on an attribute, and each leaf node represents the outcome

  • Answered by AI
Round 2 - Technical 

(1 Question)

  • Q1. How pressure varies wrt temperature
  • Ans. 

    Pressure generally increases with temperature due to the kinetic energy of gas molecules.

    • Pressure is directly proportional to temperature in a closed system (Boyle's Law).

    • As temperature increases, gas molecules move faster and collide with the container walls more frequently, increasing pressure.

    • For example, a balloon inflated indoors may burst when taken outside on a hot day due to increased pressure from higher tempe

  • Answered by AI
Round 3 - HR 

(1 Question)

  • Q1. What challenges you faced

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Interview questions from similar companies

Interview experience
3
Average
Difficulty level
Moderate
Process Duration
-
Result
Not Selected
Round 1 - Technical 

(1 Question)

  • Q1. Dropout in. Deep learning
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
Selected Selected

I applied via Referral and was interviewed before Oct 2023. There was 1 interview round.

Round 1 - Technical 

(2 Questions)

  • Q1. What is Adaboost?
  • Ans. 

    Adaboost is a machine learning algorithm that combines multiple weak learners to create a strong learner.

    • Adaboost stands for Adaptive Boosting.

    • It works by adjusting the weights of incorrectly classified instances so that subsequent weak learners focus more on them.

    • The final prediction is made by combining the predictions of all the weak learners, weighted by their accuracy.

    • Example: Adaboost is commonly used in face det

  • Answered by AI
  • Q2. Print the binary tree in different order
  • Ans. 

    Printing a binary tree in different orders

    • Use inorder traversal to print the binary tree in ascending order

    • Use preorder traversal to print the binary tree in root-left-right order

    • Use postorder traversal to print the binary tree in left-right-root order

  • Answered by AI

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
Selected Selected

I applied via Company Website and was interviewed before Jul 2023. There were 3 interview rounds.

Round 1 - Coding Test 

Binary tree question was asked

Round 2 - Technical 

(1 Question)

  • Q1. Several tech question related to past projects and random forest etc were asked.
Round 3 - HR 

(1 Question)

  • Q1. Tell me about yourself and several other questions
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Campus Placement and was interviewed in Jul 2024. There were 2 interview rounds.

Round 1 - Coding Test 

Few standard dsa questions were asked.

Round 2 - HR 

(2 Questions)

  • Q1. Explain projects
  • Ans. 

    Projects are specific tasks or assignments that require a set of skills and resources to achieve a particular goal or outcome.

    • Projects involve defining objectives and deliverables

    • They require planning, execution, and monitoring

    • Projects often have timelines and budgets

    • Examples: Data analysis project to identify customer trends, Project to implement a new software system

  • Answered by AI
  • Q2. Introduce urself
  • Ans. 

    I am a data analyst with a strong background in statistics and data visualization.

    • I have a Bachelor's degree in Statistics from XYZ University.

    • I have 3 years of experience working as a data analyst at ABC Company.

    • Proficient in using tools like Excel, SQL, and Tableau for data analysis.

    • I have experience in creating reports and dashboards to present data insights to stakeholders.

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - study dsa
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
Selected Selected

I applied via LinkedIn and was interviewed before Dec 2023. There was 1 interview round.

Round 1 - Group Discussion 

Case study about some machine learning project which i did in thr past.

I applied via Naukri.com and was interviewed in Jun 2022. There were 2 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - One-on-one 

(12 Questions)

  • Q1. What is correlation(in plain english)?
  • Ans. 

    Correlation is a statistical measure that shows how two variables are related to each other.

    • Correlation measures the strength and direction of the relationship between two variables.

    • It ranges from -1 to 1, where -1 indicates a perfect negative correlation, 0 indicates no correlation, and 1 indicates a perfect positive correlation.

    • Correlation does not imply causation, meaning that just because two variables are correlat...

  • Answered by AI
  • Q2. What is multi-collinearity?
  • Ans. 

    Multicollinearity is a phenomenon where two or more independent variables in a regression model are highly correlated.

    • It can lead to unstable and unreliable estimates of regression coefficients.

    • It can also make it difficult to determine the individual effect of each independent variable on the dependent variable.

    • It can be detected using correlation matrices or variance inflation factors (VIF).

    • Solutions include removing...

  • Answered by AI
  • Q3. What are p-values? explain it in plain english without bringing up machine learning?
  • Ans. 

    P-values are a statistical measure that helps determine the likelihood of obtaining a result by chance.

    • P-values range from 0 to 1, with a smaller value indicating stronger evidence against the null hypothesis.

    • A p-value of 0.05 or less is typically considered statistically significant.

    • P-values are commonly used in hypothesis testing to determine if a result is statistically significant or not.

  • Answered by AI
  • Q4. How are LSTMs better than RNNs? what makes them better? how does LSTMs do better what they do better than vanilla RNNs?
  • Ans. 

    LSTMs are better than RNNs due to their ability to handle long-term dependencies.

    • LSTMs have a memory cell that can store information for long periods of time.

    • They have gates that control the flow of information into and out of the cell.

    • This allows them to selectively remember or forget information.

    • Vanilla RNNs suffer from the vanishing gradient problem, which limits their ability to handle long-term dependencies.

    • LSTMs ...

  • Answered by AI
  • Q5. Does pooling in CNNs have any learning?
  • Ans. 

    Pooling in CNNs has learning but reduces spatial resolution.

    • Pooling helps in reducing overfitting by summarizing the features learned in a region.

    • Max pooling retains the strongest feature in a region while average pooling takes the average.

    • Pooling reduces the spatial resolution of the feature maps.

    • Pooling can also help in translation invariance.

    • However, too much pooling can lead to loss of important information.

  • Answered by AI
  • Q6. Why does optimisers matter? what's their purpose? what do they do in addition to weights-updation that the vanilla gradient and back-prop does?
  • Ans. 

    Optimizers are used to improve the efficiency and accuracy of the training process in machine learning models.

    • Optimizers help in finding the optimal set of weights for a given model by minimizing the loss function.

    • They use various techniques like momentum, learning rate decay, and adaptive learning rates to speed up the training process.

    • Optimizers also prevent the model from getting stuck in local minima and help in ge...

  • Answered by AI
  • Q7. What does KNN do during training?
  • Ans. 

    KNN during training stores all the data points and their corresponding labels to use for prediction.

    • KNN algorithm stores all the training data points and their corresponding labels.

    • It calculates the distance between the new data point and all the stored data points.

    • It selects the k-nearest neighbors based on the calculated distance.

    • It assigns the label of the majority of the k-nearest neighbors to the new data point.

  • Answered by AI
  • Q8. You have two different vectors with only small change in one of the dimensions. but, the predictions/output from the model is drastically different for each vector. can you explain why this can be the case...
  • Ans. 

    Small change in one dimension causing drastic difference in model output. Explanation and solution.

    • This is known as sensitivity to input

    • It can be caused by non-linearities in the model or overfitting

    • Regularization techniques can be used to reduce sensitivity

    • Cross-validation can help identify overfitting

    • Ensemble methods can help reduce sensitivity

    • It is generally a bad thing as it indicates instability in the model

  • Answered by AI
  • Q9. Slope vs gradient (again not in relation to machine learning, and in plain english)
  • Ans. 

    Slope and gradient are both measures of the steepness of a line, but slope is a ratio while gradient is a vector.

    • Slope is the ratio of the change in y to the change in x on a line.

    • Gradient is the rate of change of a function with respect to its variables.

    • Slope is a scalar value, while gradient is a vector.

    • Slope is used to describe the steepness of a line, while gradient is used to describe the direction and magnitude o...

  • Answered by AI
  • Q10. How are boosting and bagging algorithms different?
  • Ans. 

    Boosting and bagging are ensemble learning techniques used to improve model performance.

    • Bagging involves training multiple models on different subsets of the data and averaging their predictions.

    • Boosting involves training multiple models sequentially, with each model focusing on the errors of the previous model.

    • Bagging reduces variance and overfitting, while boosting reduces bias and underfitting.

    • Examples of bagging al...

  • Answered by AI
  • Q11. What is a logarithm? (in linear algebra) what is it's significance and what purpose does it serve?
  • Ans. 

    A logarithm is a mathematical function that measures the relationship between two quantities.

    • Logarithms are used to simplify complex calculations involving large numbers.

    • They are used in linear algebra to transform multiplicative relationships into additive ones.

    • Logarithms are also used in data analysis to transform skewed data into a more normal distribution.

    • Common logarithms use base 10, while natural logarithms use

  • Answered by AI
  • Q12. What are gradients? (not in relation to machine learning)
  • Ans. 

    Gradients are the changes in values of a function with respect to its variables.

    • Gradients are used in calculus to measure the rate of change of a function.

    • They are represented as vectors and indicate the direction of steepest ascent.

    • Gradients are used in optimization problems to find the minimum or maximum value of a function.

    • They are also used in physics to calculate the force acting on a particle.

    • Gradients can be cal

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - be strong in fundamentals and be able to explain what and why of every project on your resume and all things things used in those projects.

Skills evaluated in this interview

Interview experience
1
Bad
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Referral and was interviewed in Apr 2024. There was 1 interview round.

Round 1 - Technical 

(1 Question)

  • Q1. Time Series forecasting questions

Interview Preparation Tips

Interview preparation tips for other job seekers - They are happy with me send an intent to offer, but later declined as requirements changed (time waste)

Applied Materials Interview FAQs

How many rounds are there in Applied Materials Data Scientist interview?
Applied Materials interview process usually has 2-3 rounds. The most common rounds in the Applied Materials interview process are Technical, HR and One-on-one Round.
How to prepare for Applied Materials Data Scientist interview?
Go through your CV in detail and study all the technologies mentioned in your CV. Prepare at least two technologies or languages in depth if you are appearing for a technical interview at Applied Materials. The most common topics and skills that interviewers at Applied Materials expect are Python, Machine Learning, Data Science, R and Deep Learning.
What are the top questions asked in Applied Materials Data Scientist interview?

Some of the top questions asked at the Applied Materials Data Scientist interview -

  1. Intermediate level python/machine learning questi...read more
  2. Intermediate level SQL questi...read more
  3. Basic SQLQuesti...read more

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Applied Materials Data Scientist Interview Process

based on 3 interviews

Interview experience

4.3
  
Good
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Applied Materials Data Scientist Salary
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₹7.7 L/yr - ₹31 L/yr
22% more than the average Data Scientist Salary in India
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2.7/5

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1.9

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2.8

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3.0

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2.7

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