Learn what common Amazon data scientist interview questions and answers are. A data scientist is an analytics professional who collects, analyzes, and interprets data in order to help drive decision-making in an organization. Data scientists combine math, statistics, and computer science to uncover insights and knowledge from data.
Data scientists are in high demand as businesses of all sizes are looking to use their collected data more effectively. If you are looking to work in the field of data science, you need to be able to answer specific questions about your experience, knowledge, and skills. If you want to work as an Amazon Data Scientist, you also need to answer why you chose to apply for a position at Amazon and how you think you can help the company improve by using your data science skills.
In this article, you will find the top Amazon data scientist interview questions and answers that will help you ace your interview.
Top 15 Amazon Data Scientist Interview Questions and Answers
Below you will find a list of the top 15 most commonly asked Amazon Data Scientist interview questions and answers.
Q1. Tell me about yourself
This question is asked in almost every job interview. You must answer the question concisely and to the point. There is no need to walk the interviewer through your resume and background – focus on the essentials. You can highlight your main selling points and why you think you’re a perfect fit for the job.
Example: ‘As a Data Scientist with over eight years of experience, I have worked in different organizations where my skill set and experience have been tested extensively. In addition to my excellent programming skills, I excel at organizing complex data sets and visualizing it. I have excellent communication skills allowing me to explain my findings and propose solutions for complex problems accurately.
Amazon seems like a place where I can add value to your already established team of Data Scientists, and I’m looking forward to discussing the position in more detail as I’m ready to take on a more challenging role.’
Q2. Why do you want to work for Amazon as a Data Scientist?
This question is a great opportunity for you to explain why you want to work for Amazon. The answer should include specific examples that are related to the Amazon Data Scientist position.
Example: ‘I want to work for Amazon as a Data Scientist for three main reasons. First, Amazon is by far the market leader in this industry because of its determination and customer obsession. Second, this position allows me to work alongside the most talented people in Data Science, where I get the opportunity to play a part in continuing to improve Amazon products and software. This alone excites me very much. The third reason is that a former co-worker is now a Senior Data Scientist on the Amazon Data Science team. She told me about the ongoing learning and development opportunities. My ambition is to work in a workplace that promotes learning and professional growth, making this opportunity the perfect step in my career!’
Q3. How would you explain Data Science to someone who does not work in the field?
When you’re working as a Data Scientist, there will be times that you have to discuss research findings and other results with team members or others who might not have the same specific knowledge that you have. Interviewers ask this question to see if you can simply explain complex matters.
Example: ‘I would explain Data Science as a tool used to collect and analyze data from various sources, such as databases, websites, social media, and surveys. Furthermore, it involves using algorithms to uncover patterns in the collected data, building predictive models, and creating visualizations to help make sense of the findings in the data. It’s my job and responsibility as a Data Scientist to determine correct data sets and variables, collect structured and unstructured data to find these useful, actionable insights, and solve complex problems that the business encounters. In short, my work helps the company make better business decisions by leveraging data.’
Q4. What experience do you have in Data Science?
You may be asked to talk about your previous work experience and accomplishments during your interview. Of course, your resume includes details about your education and prior roles. Knowing how to answer this question will help you highlight the skills you think are most valuable to the position you’re applying for.
Example: ‘As a data scientist, I specialize in machine learning, predictive analytics, and statistical modeling. I have worked in the field for more than five years. I am proficient in developing machine learning models to solve complex problems and make predictions. In addition to Python and R, I am proficient in using Tableau and PowerBI for data visualization.
Having a background in statistics and mathematics, I have applied regression and classification techniques to various datasets. My deep learning and natural language processing skills have also allowed me to build models for text-based applications like sentiment analysis and document summarization. I have also worked with frameworks such as flask and django to build web applications. As a data-driven solutions expert, I have successfully implemented data-driven solutions for clients using my analytical mindset and technical skills.’
Q5. What is your experience with data visualization tools?
This is another work experience-related question. You must relate your data visualization experience to the position you’re applying for. You should also be able to answer follow-up questions and more in-depth questions about the tools you mention in your answers.
Example: ‘The tools I have worked with include Microsoft Power BI, Tableau, Highcharts, and a variety of other tools. Additionally, I am proficient in creating dashboards, applying filters, and manipulating data to effectively generate powerful visualizations that convey insights.
Also, I am familiar with working with both large and small datasets, and I understand how important it is to use the right visuals to maximize the impact of the data. Overall, I am confident that I can provide an effective approach to data visualization and ensure that the visualizations are effective for making data-driven decisions.’
Q6. What are your strengths as a Data Scientist?
Questions about your strengths are common in job interviews – for all levels and industries. Interviewers ask this to assess what you can do for the organization and why they should hire you. Furthermore, they are looking to understand what value you can add.
Example: ‘My strengths as a Data scientist include the ability to understand complex problems to develop creative solutions quickly. I am highly analytical, with excellent problem-solving skills and a strong understanding of qualitative and quantitative analysis. Furthermore, I’m proficient in several programming languages, such as Python, R, Java, and SQL, enabling me to work with large and complex datasets easily. Finally, I have strong communication skills and am highly organized, which helps me stay on top of deadlines and produce quality work.’
Q7. What are your weaknesses as Data Scientist?
Just like discussing strengths is important, it’s also essential that you can talk about your weaknesses. However, never use a weakness that can hurt your chances of getting the job. You must demonstrate that you are self-conscious, honest, and working on improving yourself professionally.
Example: ‘My weakness is that I get nervous when I have to speak in front of a group of people to present my research results. One of the short-term goals that I’m actively working on is improving my presentation skills. I’m taking extra communication classes outside of work while volunteering to present our most recent research results to a client. This way, I can put my new skills into practice with what I learned in the presentation course. Furthermore, it will help me develop into a team-lead role whenever the opportunity comes within the organization.’
Q8. Describe a Data Science project you worked on that you’re proud of.
Questions ask about accomplishments you’re proud of to learn more about your personality. They are interested in what makes you stand out from a pool of candidates. Interviewers want to learn more about what you find important and your core values. Your answer should give them an insight into how you approach projects. Explain what project made you proud and why.
Example: ‘A couple of months ago, I worked on a data science project that I am very proud of. The project involved building a model that would be able to predict the likelihood of customer churn in the future.
First, I identified and collected all relevant information from the company’s database, including demographics, product purchases, and customer financial information. By analyzing this data, I could create feature vectors for each customer based on their age, gender, income level, frequency of purchases, and other relevant variables.
After creating the feature vectors, I used several machine-learning algorithms to develop a predictive model. I tested the accuracy using both supervised and unsupervised methods. In the end, I was able to create an accurate model with a high prediction rate.
Through the results of this project, the company has been able to focus its marketing efforts more effectively and improve customer retention. As a data scientist, I was so excited to be able to put my skills to the test and see tangible results that positively affected the company.’
Q9. How do you deal with missing or incomplete data?
As an Amazon Data Scientist, you will likely encounter situations in which data is missing or incomplete. This question is asked to assess your problem-solving and creative thinking skills. Explain how you deal with these types of situations and how you solve them.
Example: ‘Having to deal with missing or incomplete data is a reality of working with data science projects. The first step in dealing with incomplete or missing data is identifying it. The data can be inspected visually, descriptive statistics can be used to perform this analysis, or data profiling techniques can be used.
Several strategies can be employed once missing or incomplete data has been identified. Generally, the most appropriate approach will depend on the project’s specific context and requirements. Among these are imputation (filling in missing values with estimates), deletion (removal of records with missing values), and multiple imputation (generating estimations of missing values based on predictive models).
Additional data sources may sometimes need to be gathered to supplement existing data. These could be external data sets or experiments that collect new data. The final step in the research process should be documenting any decisions made regarding missing or incomplete data and their effects on the data set and results. This will facilitate a better understanding and reproducibility.’
Q10. How do you go about debugging machine learning algorithms?
This question is a great way to assess your problem-solving skills and ability to apply them to situation you will likely face on the job. When answering this question, its helpful to walk the interviewer through the steps that you would take to solve this problem.
Example: ‘Debugging machine learning algorithms in a data science project can be approached in several ways. Firstly, it is important to clearly understand the problem you’re facing and the model being used. This allows me to identify potential errors or issues in the algorithm that may be causing unexpected results.
The next step is to inspect the dataset, checking for any outliers or incorrect labels influencing the model’s performance. If any issues are identified, they should be addressed through data cleansing or correcting the algorithm itself. It is also essential to compare the model’s performance against benchmark results.
The next step is to look at the hyperparameter settings, which may need to be adjusted depending on the application. A grid search or other optimization technique can be used to find the optimal settings for the model. Finally, if all else fails, it is possible to use debugging tools, such as TensorFlow Debugger, to identify any issues in the code.’
Q11. What do you think makes a successful data scientist?
Employers ask this question to learn more about what you think is important in the role on a day-to-day basis. When answering this question, think of a couple of important things that are essential to succeed as an Amazon Data Scientist.
Example: ‘In my opinion, in order to succeed as a data scientist, you need to be able to excel at analytical work, problem-solving, and communication. You need to understand how to work with data and technology and be able to extract meaningful insights from large datasets. Furthermore, a deep understanding of the business context in which you operate is essential. The ability to communicate complex ideas effectively and being able to think strategically about how to use data to make decisions is crucial. Besides that, I think that data scientists need to stay up to date with the latest trends in technology, programming languages, and data sciences and use this knowledge to drive innovation within the organization.’
Q12. What do you like and dislike most about being a Data Scientist?
Employers are interested in what you like about your work to gauge whether you will be a good fit for the job. They want to know what you enjoy about going to work everyday. On the other hand, employers want to know what you dislike about your work as well. They ask this to find out whether or not you are going to be happy in a new position within their company. Furthermore, based on your answer they can learn more about if they can provide you with the right opportunities and challenges that you are looking for.
Example: ‘I enjoy being a Data Scientist because I get to explore data and uncover new insights, constantly challenging myself to derive meaningful conclusions based on the analysis and visualization of data. I am also able to work with a wide range of datasets, including large corporate data sets and smaller personal ones. Finding patterns and correlations in any type of data is always exciting.
The only thing you could perhaps say that I dislike is that it’s sometimes hard to work with business counterparts who can’t articulate what they want from a data set. However, I possess strong communication and interpersonal skills, which help me ask the right questions to help and figure out what they need. Sometimes this takes some time, but it’s for the better for the project and making sure I get it right the first time.’
Q13. Can you explain the concept of supervised and unsupervised learning?
Amazon interviewers are interested in your knowledge and sometimes assess this during an interview. A question such as explaining the concept of supervised versus unsupervised learning can be one of them. Explain to the interviewer shortly and concisely the differences between the two.
Example: ‘In supervised learning, input variables (features) and output variables (targets) are used to train an algorithm to predict outputs or outcomes based on labeled data. A goal of supervised learning is to accurately predict the target labels from the input data, which can then be used to make decisions. Examples of supervised learning include linear regression and decision trees.
Unsupervised learning is a machine learning technique that focuses on uncovering patterns in unlabeled data. It applies algorithms to data to discover hidden structures and relationships between variables. Unlike supervised learning, unsupervised learning does not require any training data. Examples of unsupervised learning algorithms include clustering, anomaly detection, principal component analysis, and deep learning.’
Q14. How would you deal with a client or manager who pushed back on your data science recommendations?
In Data Science, you will encounter situations where you get pushed back on your recommendations. In these situations, it’s important that you can explain why you made certain decisions and the value behind your research. Include these aspects in your answers to explain to the interviewer how you go about resolving these situations.
Example: ‘In order to understand why a client or manager opposes my data science recommendations, I would first try to understand why they oppose me. To address their concerns and objections more effectively, I will need to understand them clearly. My next step would be to explain how I came up with the recommendation using data science, highlighting any assumptions I made and how they were tested and validated. Also, I will explain the implications of failing to follow the recommendation and its possible consequences. I would also be open to alternative solutions and listen to the client’s or manager’s feedback. I believe that open dialogue and discussion are key to finding a resolution that works for both parties.’
Q15. Why do you want to leave your current job?
This question is a great opportunity to sell yourself to the Amazon hiring manager. Give the interviewer an answer that puts you in the spotlight without you having to degrade your previous employer or the company you worked with. Explain to them why you are looking for a new job opportunity so that they know you put actual thought into your job search.
Example: ‘I feel it’s time for a change for several reasons. Although I really enjoyed working for my current employer, I feel that I have outgrown the position. There’s very limited room for growth, and I’m ready to take on new and adventurous challenges in a company that offers much room for expansion. While I’m sad to leave my current job, I look forward to a new and fresh start in an innovative environment where I can utilize the skills and work experience I have acquired.’
Conclusion Amazon Data Scientist Interview Questions and Answers
Your goal during an interview where you get asked Amazon Data Scientist interview questions is to highlight your skills and experience. Interviewers want to hear you talk about why you think you’re a valuable asset to the company. Make sure you give the interviewer concise and to-the-point answers.