Amazon Data Scientist : 6 Excellent Ways to become One | DataTrained

Rahul Singh Avatar

Introduction Amazon Data Scientist 

The demand for data scientists is skyrocketing around the world. If you want to know how to become an Amazon Data Scientist then you should surely read this article. If you want to deal with terabytes of extensively interesting data or you have a keen interest in acquiring a skill to manage a pile of data and manage it for Amazon, then this article is for you. Did you know that Amazon is among the top 3 companies in which Data Scientists aspire to work?

A new generation of analytical data professionals, the data scientist, is a relatively new vital actor in enterprises. They are a mix of mathematicians and computer scientists who govern the big data world. Businesses nowadays are dealing with massive amounts of unstructured data, which may be a virtual gold mine if mined properly.

They do, however, require people who can Dive into the data and extract useful business insights, sifting through the chaff to discover the gold nuggets. That is what a data scientist does, which is why they are in high demand and well-compensated.

The benefits of being a data scientist are attracting many IT professionals to choose this career path, whether it’s the thrill of problem-solving or the high salary.

Amazon is regarded as one of the best places to work for data scientists, with competitive pay and intriguing career options. Amazon’s business is always expanding as it strategically and successfully meets the needs of its clients. Amazon’s marketing plan is one that can benefit any company, large or little. 

Major corporations are looking to hire data scientists and are willing to pay a satisfiable amount. When it comes to employing data scientists, Amazon has set extremely high requirements. At Amazon, the job of a data scientist is determined by the team. AWS, the Supply Chain Optimization Technologies (SCOT) forecasting team, Alexa, the NASCO Team, the Middle Mile Planning Research and Optimization Science (mmPROS) team, and others are among these groups.

According to the research, the following organizations have the largest data-related workforces across all titles: data scientist, data engineer, data architect, database administrator, machine learning, big data engineer, and AI: 


Amazon Data Scientist Work Force

Being prepared is considered to be the most important factor in getting the most satisfactory result. Consider a situation in which you went for an interview on the basis of your skillset but failed at behavioral skills. This will lead not only you but your career down. 

Here are the different types of Amazon Data Scientist teams: 

  • Data Analytics

This position is primarily responsible for forecasting, finding strategic opportunities, and offering informed business insights. Tableau and other data visualization technologies, as well as data warehousing expertise, are frequently required.

  • Researchers in Machine Learning

This position focuses on cutting-edge research in fields such as natural language processing, deep learning, video recommendations, streaming data analysis, social networks, and so on. The posts often range from PhDs to internationally known researchers.

  • Applied/Data Scientists

The data scientist is the most common and well-known role, and it entails diving into large data sets in order to build large-scale simulations and experimental systems, develop optimization algorithms, and harness cutting-edge technology across Amazon.

  • Data Engineer

This group is responsible for creating tools or products that are used both inside and outside the firm. Consider AWS or Alexa. The role is quite similar to that of an ML Engineer. Skills in object-oriented languages such as C++ or Java are frequently required.

The Hiring Process For Amazon Data Scientist India 

Every company has its own process of hiring which can decide how an interviewee will be the best asset for the company. Amazon India’s recruitment process has six main steps i.e, 

  • Resume Screening 
  • Telephonic round 
  • The Hiring Manager Round
  • Writing Test 
  • Loop Interviews
  • The Hiring Committee reviews 

Amazon Data Scientist – Basic Job Description

An amazon data scientist is someone who creates value from data. Do you have any idea what a data scientist’s job entails? No prizes for guessing that a data scientist’s day revolves on data and data everywhere, as the job title suggests.

The job description of a amazon data scientist includes gathering data from numerous sources and analyzing it to gain a comprehensive picture of how a business operates. To automate certain operations inside the firm and offer smart solutions to business difficulties, scientists use statistical and analytical approaches as well as AI tools. They deliver the results in a straightforward and engaging manner after interpreting the data. The goal is to assist the organization in analyzing patterns and making better decisions. As a result, a competent data scientist must possess the correct mix of technical, analytical, and communication abilities.

Let’s start with the fact that the data scientist role evolved and extended from that of a data analyst. Within an enterprise, both the data analyst and data scientist organize and analyze massive data sets. However, an amazon data scientist also has the responsibility of influencing how the company approaches business difficulties by applying business acumen and communication skills.

To obtain outcomes, amazon data scientists combine practical abilities like computing and math with the ability to perform statistical analysis. For example, A amazon data scientist working for a social networking site might examine the types of pages people ‘Like’ and, based on that, determine the types of adverts users will see when they check in.

Skills Tested in the Amazon Data Scientist Interview 

An example of a job description for an amazon Data Scientist posted recently in 2022. 


Job summary: Our customers have immense faith in our ability to deliver packages timely and as expected. 


A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion or operational breakdowns) and perform proactive corrective actions. 

When failures do happen, it has inbuilt redundancies to mitigate impact (such as determining other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). 

Finally, it is cost-optimal, so that customers can be passed the benefit from an efficiently set up network. Core Transportation Technology is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Core Trans, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). 

You will build models to improve the quality of financial and planning data by accurately predicting off-manifest at a package level. Your models will help optimize shipping cost by improving quality of upstream data (such as through corrections to weights and dimensions in product catalog). 

Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. 

Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models.

You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modeling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). 

You will partner with Applied Scientists and Research Scientists from other teams in the US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements.

Interview Process for Amazon Data Scientist

Amazon’s data scientist interview procedure is similar to that of other IT corporations. The Amazon Data Scientist interview process includes a technical and behavioral screening via a phone interview, followed by a technical phone screen and, finally, an in-person interview.

The organization evaluates a candidate’s knowledge of programming languages such as Python, Java, and SQL during the technical interview. It ensures that a candidate is well-versed in statistics, mathematics, data mining and data extraction, as well as the entire data pipeline. It also examines a candidate’s knowledge of machine learning and data visualization software.

The organization uses behavioral screening to get to know a candidate, assess their communication skills, assess their problem-solving ways, and see examples of the 14 leadership traits.

Basic Requirements for Amazon Data Scientist 

There is space for specialization within each of Amazon’s teams. For instance:

  • To train Alexa to comprehend and understand voice commands across different languages, data scientists working on Alexa may need a Ph.D. and specialised understanding of natural language processing and information retrieval.

  • A data science specialist working on demand forecasting will have to develop sophisticated algorithms that involve learning from large amounts of data such as prices, promotions, similar products, and product attributes in order to forecast the demand of more than 190 million products sold on worldwide.

  • An Amazon data scientist working for Amazon Web Services will help AWS customers construct machine learning models to solve their business objectives.

  • A Ph.D. in Machine Learning, Data Analysis, Statistics, or a related field is required to work as a Data Scientist at Amazon. His or her math abilities will be just as important as their programming abilities. Candidates should have: A master’s degree in statistics, computer science, mathematics, physics, computational biology, or economics, or comparable practical experience is required.

  • R, Stata, MATLAB, Python, SQL, C++, or Java are examples of statistical software packages and functional programming languages.

  • Knowledge of how to create and deploy machine learning algorithms that are tailored to specific business demands and evaluated on huge datasets.

  • Data mining and database experience in a business setting with large-scale, complicated datasets.

  • Hands-on experience with machine learning, data extraction, analysis, and communication in an analytical role.

  • Outstanding oral and written com
    munication abilities.

A Personal Experience of an Amazon Interview 

Amazon Data Scientist- Common Interview Questions 

Behavioral Questions 

There are various types of questions asked by an interviewee at the interview for Amazon Data Scientists, some of them are listed below by their categorization.

  1. Fit Questions

Fit questions are a sort of question used to determine whether or not a candidate is suitable for the position for which they applied. They will cover a variety of topics, including agile technique or workflow, cooperation and collaboration, and dispute resolution, depending on the role of Amazon Data Scientist.

Some Examples: 

  • Amazon is an odd company. What makes you so unique?

  • Would you stand up to a boss who made a choice that went against company rules and put one of your colleagues in danger?

  • Tell me about a situation when you had to go above and beyond management to get your point across.

  • Tell me about a time when you were in charge of a group and were given a goal to achieve.

  • Tell me about a time when you were involved in a group conflict and how you dealt with it.

  • What activities did you take as a leader to boost productivity?

  • Tell me about an instance when you had to cope with a subpar employee.

  • How do you feel about leadership?

Here is How to approach it: 

Remember that the goal of behavioral questions is to see if you’re a good fit for the job. As a result, the goal is to develop three to four stories that are tailored to the individual job requirements (professional experience, attributes, character, etc).

Prepare 3-4 anecdotes about your technical experiences, and don’t forget to include attributes that make great software engineers (superior communication skills, quick learning ability, strong team player, etc), in addition to the aforementioned Amazon traits, if you’re applying as a software engineer.

Here are some tips for you:

  • Lay the Foundation for the Content:

Compare your past experiences with Amazon attributes and your most cherished personal values, then choose the stories that best express those traits and values.

Make a list of as many facts as you can about your stories, and make sure they follow this format: Problem, Actions, Result, Lesson.

  • Form the Storyline of the Story:

Remove any unneeded details, clarify technical terms to assist listeners understand, and then rearrange and dramatize the remainder to make your accomplishments truly stand out.

Emphasize the relevant features, present your story in an organized manner, explain all of your activities, and so on to bring the Amazon spirit into the mix.

  • Improve Your Look

Both you and your audience should find your storytelling style entertaining. Take the time to practice and develop your approach — remember, it must be natural or you won’t be able to apply it in a high-pressure, high-stakes interview.

Because we’re talking about a job interview, keep in mind that your style should be official. It’s not a stand-up comedy show, so don’t use your usual sarcasms.

Technical Questions

Technical questions are only asked of candidates who are applying for technical positions such as Software Engineer, Electrical Engineer, Test Engineer, and Network Engineer, to mention a few. The following are some of the most common types of interview questions: Arrays, linked lists, trees, strings, dynamic programming, arithmetic and statistics, backtracking, graphs, design, sorting, and searching are only some of the topics covered.

  • Trees and graphs (46% of questions, most frequent)

  • Strings / Arrays (38%)

  • Lists with links (10%)

  • Sort / Search (2%)

  • Queues / Stacks (2%)

  • Tables with hashes (2% of questions, least frequent)

Here are some Questions for Amazon Data Scientist. 

Beginner Level

  • Tell us something about AWS.

  • Was there any time when you had to deal with ambiguity?

  • Have you ever worked in a team? 

  • Which project have you worked on?

  • How do you plan your time management strategies?

  • What is a confusion matrix?

  • Describe Markov Chains.

  • What is a true positive rate and a false-positive rate?

  • What is dimensionality reduction?

  • How do you find the RMSE and MSE linear regression models?

 Question Related to Machine Learning

  • Briefly describe the Decision Tree Algorithm.

  • What are Entropy and information gained in the Decision Tree Algorithm?

  • Differentiate between Regression and classification ML techniques.

  • What are Recommender Systems?

  • How can outlier values be treated?

  • What are the various steps in an analytics project?

  • During analysis, how do you treat missing values?

  • How will you define the number of clusters in a clustering algorithm?

  • Describe Ensemble learning.

  • How do you work towards a random forest?

Questions Related to Deep Learning

  • What is Deep Learning?

  • Explain Neural Network fundamentals.

  • Describe Neural Networks.

  • What are Hyperparameters?

  • Differentiate between Epoch, Batch, and Iteration in Deep Learning.

  • What are the different layers on CNN?

  • How does Pooling work on CNN?

  • Explain how the LSTM network works.

  • Define the Gradient Descent.

  • What are exploding gradients?

Question Related to Coding

  • Describe JOINs and SQL.

  • What is the most advanced query you’ve ever written?

  • White a SQL code to explain the month-to-month user retention?

  • You are given a list of integers, and you need to find a certain element. Which algorithm will you use?

  • Which algorithm would you use to search a long list for the four elements if you have a long and short sorted list?

  • Write a Python function that displays first N Fibonacci numbers.

  • What are the processes of improving a classification model of low precision?

  • You are given time-series data by the month with large data records. How will you find differences between this month and the previous month?

  • How do you inspect missing data?

  • When is the missing data inspection important?

To know more about this please check this link : 

 Some Tips For Cracking The Amazon Data Scientist Interview

  • When it comes to data science positions, Amazon places a premium on technical ability. Remember to brush up on algorithm solving, query optimization, and understanding how various machine learning algorithms function behind the scenes.
  • Every applicant is evaluated based on their 14 leadership principles. Try to remember all the 14 leadership principles because you will be asked to demonstrate them throughout the behavioral interview. Consider your previous initiatives or experiences to see how you have shown those concepts.
  • Modeling, machine learning, and business case problems are all things to practice. Amazon will almost certainly offer you a series of uncleared case questions in which you must apply machine learning to a commercial context. Take a look at how we handle a case.

Additional Amazon Data Scientist Interview Questions 

  • Write a program to see if the smaller string can be formed from letters in the larger string given a large string and a smaller string. 
  • The chances of finding an item at site A are 0.6, and 0.8 at site B. What are the chances of finding that item on the Amazon website?
  • Implement the intersection and union of two arrays (in an efficient way). The elements of the two supplied arrays can be repeated, but the union and intersection arrays cannot.
  • In HDFS, you have two files. One has a two-column date range: the start date and the end  date. Another file includes two columns, one for the date and the other for the number of visitors. 
  • Use an array, create a circular queue.
  • What’s the difference between Ridge Regression and Lasso?
  • Users interact with the Amazon website in a variety of ways, such as clicking on buttons, conducting searches, and so on. What is the best strategy to predict whether their next move will be to buy something?
  • How does the K-means algorithm work? Which type of distance metric would you pick? What if the dynamic range of different features varies?
  • What are the differences between generative and discriminative algorithms? What are their advantages and disadvantages? Which algorithms are most commonly employed, and why?

Amazon Data Scientist Earnings Level

Amazon Data Scientist Salary at Entry Level 

Fluency in at least one programming language, such as Python, Java, or PHP, and knowledge of SQL are often required for entry-level data science careers, including internships and responsibilities held by people actively seeking advanced degrees.

In addition, applicants must demonstrate that they have solved analytical challenges using quantitative methodologies and that they are committed to Amazon’s 14 leadership values. The typical starting pay for an Amazon data scientist is roughly an average of 13–14 LPA with an additional pay average of 2–3 LPA. which includes cash bonuses, stock bonuses, profit sharing, commission sharing, and tips. 

Amazon Data Scientist Salary at Sr. Level 

A bachelor’s degree in computer science, data science, statistics, mathematics, engineering, economics, or a similar discipline is often required for senior-level data science employment that isn’t management positions. A master’s degree or Ph.D. in machine learning, natural language processing, or computer vision may be necessary, in addition to prerequisite knowledge of coding and scripting languages and proven experience working in analytics, depending on the area of concentration.

The average salary for a senior-level Amazon data scientist is roughly 30–35 LPA. 

Amazon Data Scientist Salary in India 

For 5 to 8 years of expertise, the average Amazon Data Scientist salary in India is 41.7 Lakhs. Senior Data Scientist salaries at Amazon range from 25 to 75 lakhs. It is 76 percent more than the typical Senior Data Scientist Salary in Internet Companies, according to our estimations.

Here are some examples of different pay range of data scientists at amazon within India:


If you want to know how to become a Data Scientist at Google, CLICK HERE… 


Most data scientists aspire to work for multinational or multi-billion dollar companies so that they can get the maximum output from their skills. Hence, Amazon is one of the shark companies that is ruling all over the world with its amazing teams of different departments. 

While working for a company like Amazon, a data scientist will learn and inculcate all the necessary skills and knowledge one has to have. This article guides you to the various attributes of an Amazon Data Scientist and gives you brief and concrete information about what id the role of an amazon data scientist? How to crack the interview for amazon data scientist? And How much does an amazon data scientist make ?

Becoming an amazon data scientist may be difficult but it comes with pride and pleasure. Once you became an amazon data scientist, you will gather all the perks and benefits of working in a company which is customer as well as employee friendly. 

Frequently Asked Questions’

What is the Role of an Amazon Data Scientist?

Data Scientists serve as the link between Amazon’s business and technical sides; they can convert and model enormous data sets while offering significant business insights to stakeholders.

Does Amazon hire data scientist?

Amazon is regarded as one of the best organisations for data scientists, with competitive pay and intriguing career options. Continue reading to learn how to become an Amazon data scientist. The global need for data scientists is at an all-time high.

What are the different job levels at Amazon?

Amazon assigns the following years of experience to each level:

  1. Experience Level 4: 1-3 Years
  2. Level 5: Three to ten years of experience.
  3. Level 6: 8-10 years of experience is required.
  4. Level 7: 10 or more years of experience Even at this level, Amazon likes to promote from within and seldom hires outside talent.

Which IT business has the most data?

By far the majority of your information is collected and stored by Google. This is hardly unexpected given that their business strategy is based on having as much data as possible (and making it super easy for you to access).

What is the difference between an Amazon Data Scientist and data scientist at other big companies?

Every data scientist works with various kinds of data or product enhancements. It depends on the need of the hour that is crucial for product development. An Amazon data scientist may work on alexa, AWS, Cloud computing, etc.

How can I become a senior amazon data scientist?

First you have to gain essential knowledge and the desired experience for about 10+ years. Then you can become a senior data scientist. 

What is the salary of an Amazon Data Scientist in the USA?

The annual salary for an Amazon in the United States is around $139,013, which is 14% more than the national average.

Do data scientists make use of AWS?

Every data science practitioner, from data scientist to data analyst, must understand AWS and how it operates.

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