The name Amazon today is very prevalent. Every household has at least once associated themselves with their services and has appreciated their ever-growing service. Being the world’s largest eCommerce industry, Amazon strives to make the best use of the latest technologies and always puts forward its best in terms of innovation. As the company iterates, the data collected gets larger and larger, and to handle this data Amazon is always looking for motivated data scientists that could meet their data demands. Let’s know some Amazon Data Scientist Interview Questions.
Amazon holds a very competitive spot for its data scientist recruitment. The interview process associated with them is precisely categorized in three rounds along with internal rounds that test the person on a different range of data modeling knowledge.
Let’s dive deeper into the further role and responsibilities of the data scientist at Amazon. Stay tuned to the article as we have top interview questions for data scientists that can improve your chance of getting hired.
What is the role of a data scientist at Amazon?
Amazon being a vast company often tends to be a melange of more than one department that looks after various associations of the companies. Depending on this association data scientists are often divided into teams that require conglomeration of the data.
Amazon has widely classified its technical team based on web service, forecasting, Alexa, Supply chain optimization, planning and research optimization team, and others. Each team has one data scientist associated with them that looks after the design, development, and evaluation of the team.
The major technologies associated with data scientists are Machine Learning (ML), Natural Language (NL), Neural network, and others. A data scientist often builds accurate prediction models that deploy solutions for problems that arise in the system, it also generates algorithms that make better use of the system’s existing knowledge to improve the working of the system and meet the customer’s demands according to their needs.
Depending on the type of work a data scientist does they are majorly classified into the following categories:
- Machine learning research scientist
- Business Intelligence and data analysis
- Applied scientist
- Data engineer
The interview process
The Amazon interview is very competitive. The company hires candidates on stringent interview policies and often tests the candidate’s capabilities on several different criteria. The interview is divided into three rounds namely: Initial round, technical screening, and an onsite interview. The onsite interview is further divided into 5 personal rounds that can last for up to 6 hours with occasional breaks.
Initial round:
The initial round is a telephonic interview that can be conducted via video call on any platform like skype or meet. This round tests the candidates on their technical as well as behavioral aspects. The questions of the round are basic conceptual questions to test whether the candidate is good for the company or not.
The round also takes into consideration a person’s behavioral aspect and briefly discusses a person’s background and past science in the work culture. A candidate needs to be thoroughly prepared and confident to crack this interview.
Tip: The questions often range in the basic concept category. Try to grasp the basics and also talk about your projects and experiences from past work. This can help you to digress questions to your mainstream.
Technical screening:
The technical screening is purely based on the technical aspect of the job. In this, a candidate can expect a minimum of two questions from SQL, python, machine learning, or data science.
The screening is carried out through an online coding platform and also involves video interviews to discuss the basic problems faced and solutions to the coding round. A candidate needs to understand the thought process behind every question and needs to express them to the interviewers during coding.
Maintaining the pace of your thinking and discussing it with the interviewer will prove beneficial. This allows the person to understand your approach to the problems and also know how you bring out solutions to the problem
The technical round will also include some basic questions regarding the Machine learning concepts. Make sure to brush the concepts up and discuss them with the interviewer through an open-ended process.
Tip: The only tip for this round will be to brush your coding skills and syntax knowledge. Try to have multiple approaches to a single problem as this will allow you to think from different perspectives and allow for error space that you can easily fill.
Onsite interview:
Onsite interview is one of the most dreaded rounds of Amazon interview as it involves 5 internal rounds with experts from various departments. As the interviews are from five different categories like data analyst, business intelligence, hiring manager, machine learning expert, and business expert leader, one needs to be thorough with their knowledge to impress them.
The onsite interview will have questions that are based on technical aspects of advanced level, behavioral questions, and leadership questions that adhere to the policies of Amazon’s 14 leadership principles.
Tips:
- Amazon looks for very technically sound people for roles like data scientists. Make sure to have a thorough understanding of the topics that are covered under this role and practice as many questions as possible for the interview.
- Practice leadership questions as they will also prove important in terms of solution building, and personality analysis.
Data Scientist Interview Questions
- If the probability of seeing a shooting star in 15 minutes is 0.4, then what is the probability to see the star in a period scattered over 1 hour?
The probability of not seeing a shooting star for the first fifteen minutes will be.
= 1- the probability of seeing the star.
= 1-0.4 = 0.6
Now, 4 times 15 minutes makes for an hour. The probability of not seeing the shooting star in 1 hour will be
= (0.4)^4 =0.0256
Now, the probability of seeing the shooting star will be.
= 1- ( probability of not seeing the star in one hour)
= 1- 0.0256 = 0.9744.
- Explain the steps in making a decision tree?
- Take the entire data provided as the input.
- Find the entropy and predictor values of the target variable.
- Calculate the different attributes and measure the gain obtained in each attribute.
- The attribute with the highest gain acts as the root node and the same procedure is continued until every branch is finalized.
- How would you build a random forest tree model?
The first step to build a decision tree would be to divide the random model into packages and form different trees for different objects present in them. When compiled together a random forest model is generated that decides at the end.
- Select features from the ‘m’ feature such that a<<m.
- Calculate the node according to the best-fit point D.
- This allows the node to be split into daughter cells. The steps should be repeated until all leaf nodes are finalized.
- Build an entire forest by repeating the entire sequence of steps for n times.
- The entire process will leave you with an ‘ n’ number of trees.
- How would you avoid overfitting in your model?
There are potentially three methods that can be used to avoid overfitting the model.
- Simple approach: take less data that could eventually avoid overfitting. This will also remove unwanted noise in the data and keep the system away from overfitting.
- Uses cross-validation techniques.
- Different regularization techniques can also be used, this penalizes the data that causes overfitting.
- What is dimensionality reduction and how could it be useful?
Dimensionality reduction helps to reduce a set of data that has wide dimension to concise data that carry similar information.
This significantly reduces the storage space and completion time of the data. It also avoids redundancies.
- How would you maintain a deployed model?
The deployed models can be painted using a few simple steps.
Monitoring:
Models need to be monitored from time to time to about a precision accuracy meter that allows the how changes affect the performance of the model and what brings precision.
Evaluate:
The evaluation matrix is designed for the current matrix to determine whether a new algorithm is needed or not for accuracy.
Comparison:
If a new algorithm or model is defined, they are compared to each other to draw a precision matrix and find what works better for the existing model.
Rebuild:
The existing or the best model is rebuilt according to the requirements of the model and the current state of data.
6. What is A/B testing and why is it used?
A/B testing is used to hypothetically randomize two variables in an experiment.
The final result of the A/B testing is to check for changes in the web page. This allows them to find promotional events and marketing strategies that work best for a product.
Coding questions:
- If two strings are given that differ in size, determine the smaller string from the larger string without repetitions.
- Take two arrays and find union and intersection. The original and the new array should not overlap.
- Give the syntax to modify a table with a billion rows.
In conclusion, all you need to do is brush up on your knowledge and polish those coding skills. Amazon is extremely competitive and always looks for people who are technically sound and creative.
So go through the roles, interview questions, and the process of the interview again so that you don’t miss the chance to get hired at one of the most renowned companies – Amazon.
