This post provides you the information you need to prepare effectively for the Amazon data scientist interview and come out successful.
It presents valuable interview ideas and practice questions and answers that you can use to make your data scientist interview with Amazon a breeze.
Please, read on:
20 Amazon Data Scientist Interview Tips
Here are very important tips to use in your preparation for an interview with Amazon for the data scientist position:
- Refresh your machine learning, SQL query, and statistics fundamental knowledge.
Going over technical foundations prepares you to answer technical questions better.
- Research Amazon 14 leadership principles.
Amazon measures candidates against their very crucial 14 leadership principles. You should know them to get hired for the data scientist job.
- Expect and prepare for different types of questions.
Data analysis questions often involve three or more different types of questions; practice modelling, machine learning, and business case questions.
- Interviewers will test your ability to work with big data.
You will have to deal with large and complex sets of data in Amazon. Some of the interview questions will test that ability. Get ready for them.
- Approach problems from a product perspective.
It’s okay to display your technical prowess. But more than that, Amazon wants you to solve practical product problems. Make sure to answer questions from product perspective.
- Highlight your thought process.
Don’t tell the solutions to problems; take interviewers through your journey to finding solutions.
- Translate data findings into actionable advice.
Every data finding should be concluded with some practical advice.
- Engage their products and try to improve them.
Demonstrate your desire to improve Amazon products by relating answers to their products as much as possible.
- Demonstrate decisiveness in data ambiguity.
Decisiveness is one important value Amazon wants to see in employees, especially data scientists who come across ambiguous sets of data during analysis.
- Show capacity to evaluate from multidimensional angles.
Your ability to interpret problem situations from different angles and perspectives is a core data scientists’ skill. Amazon will be watching out for it.
- Effective communication skills are important.
Working situations demand a lot of exchange of data across different teams. Interviewers want to know you are capable of doing that.
- Research current projects and research developments in the company.
Current projects in Amazon are mostly used as product case study questions. You have to do your research well.
- Research Amazon key products so well before the interview date.
Data analysis product case questions measure your sense of business intuition around product mechanics. You can’t answer the questions if you don’t know their products well enough.
- Improve your problem-solving skills before the interview.
Some basic soft problem-solving skills will be vital all through the interview.
- Expect behavioral questions too.
Most data scientist candidates don’t expect or prepare for behavioral questions. Don’t make that mistake.
- Use STAR technique to answer behavioral questions.
STAR stands for situation, tasks, action, and results. These techniques will help you answer questions with more clarity and precision.
- Always give enough details and explanation.
Don’t answer questions with punch lines and few jargons only, give details.
- There is often no right or wrong questions to study questions.
Be yourself while answering study case questions. Present your analysis and solution clearly and confidently.
- Always ask questions when you need clarity.
Sometimes, study case data and questions might be vague. You will need to ask the right questions to come up with good answers.
- Rehearse practice questions and answers.
Studying practice questions and answers prepares you better for the interview.
20 Amazon Data Scientist Interview Practice Questions and Answers
Here are sample questions and answers to work with in your preparation for a data scientist interview at Amazon:
- How are categorical responses predicted?
Classification technique is implored in classifying data sets.
- What is power analysis?
It is an experimental design technique used to determine the effect of a particular sample.
- What is collaborative filtering?
It’s a system of filtering that is used by recommender systems to identify patterns by collaborating multiple viewpoints and data sources.
- What is the difference between the mean value and the expected value?
There is no difference between the two of them. Context decides which one of them to be used. Mean is often used in the context of probability distribution while value often refers to random variable context.
- What purpose does p-value serve?
P-value is useful for determining the meaning and significance of results after a hypothesis test in statistics.
- Can machine learning be used for time series analysis?
Yes, it can. But this depends on the application.
- What does NLP stand for?
It stands for Natural Language Processing.
- What purpose does NLP serve?
It is a branch of artificial intelligence that enables the machine to read and understand human languages.
- What are the types of selection bias?
They are sampling bias, time interval, data, and attrition.
- What is overfitting?
Overfitting is an error that leads to inaccurate prediction of new data points. It happens when the model fits the data too well, leading to a model with high variance and low bias.
Other questions you should watch out for in an Amazon data scientist interview include:
- What is the best way to model if the next action of Amazon users navigating through the website would be to buy a product amongst other possible courses of action?
- Explain SVM.
- Explain boosting.
- What is the difference between MLE and MAP inference?
- Execute circular queue using an array.
- Find the cumulative sum of the top 5 most profitable products of the last 10 months in New York.
- What is gradient checking, why is it important?
- Explain logistics and linear regression.
- Talk about the topic modelling techniques you know.
- What is the difference between Lasso and Ridge Regression?
10 Questions you can ask Interviewers in an Amazon Data Scientist Interview
Asking good questions helps the interview process and results. Here are 10 good ones you can ask if you are taking a data scientist interview with Amazon:
- What are the top challenges a data scientist would likely face in Amazon?
- What does it feel like occupying a sensitive position as a data scientist in Amazon?
- What skills and experiences are you looking for in an ideal candidate?
- What is the toughest job you have had to do as a data scientist in Amazon?
- How do I compare with other data scientist you gave an interview in the past?
- Is there anything you would need me to clarify?
- What projects are the company working on now?
- What does the career path of a data scientist here look like?
- What do you think I should know about the position I am applying for?
- What common mistakes do people make in this role?
If you are taking a data scientist interview with Amazon and desire to pass it, you should stick with the tips shared on this page and practice the interview questions also provided here and you will be taking a major step towards being hired in Amazon.