What Does an Amazon Data Scientist Do?

What Does an Amazon Data Scientist Do?

As a rising data scientist, you’re not far from becoming one that works at Amazon. Many of the skills required to be an Amazon data scientist are exactly the same as those mandatory for all data scientists out there, but with some additional touches.

To start off, you must know that the roles and responsibilities vary according to the type and scale of the company. As an Amazon data scientist, your resume has to be perfect and you should be prepared for multiple screening tests and the final stage interview.

Apart from the basic skill set, a certain level of qualifications is preferred by an Amazon data scientist. This is to ensure that you can meet the requirements of the daily tasks.

In this article, we’ll go what an Amazon data scientist does, their role, responsibilities, and more.

Let’s get started.

How Do I Become an Amazon Data Scientist?

You can only be a good candidate for this position once you’ve understood Amazon’s goals and vision. Once you’ve figured out how Amazon works on a daily basis, you can probably discern the role of data scientists working at Amazon.

Amazon is an American multinational company with a diverse workforce across the globe. It is one of the world’s largest e-commerce platforms that also specializes in cloud computing, digital streaming, and AI.

One of the main objectives of Amazon includes continued innovation in the product mix that they offer. Therefore, they aim to attract the best, creative and innovative minds. Furthermore, Amazon seeks these attributes in candidates for all departments, including data science.

Does Amazon Hire Data Scientist?

A great many factors combine to make data scientists a crucial part of the Amazon workforce.

  1. A large number of products and services

The company offers a wide range of products and services to its international customer base. This means a constant flow of data and, therefore, a need for data monitoring. Above all, there is a need for problem-solvers for everyday issues in data science.

This includes categorizing data and ensuring a smooth flow of day-to-day operations. In addition, Amazon data scientists are required to come up with future prospects in data science for a better performance of the overall company.

  1. Customer-centric Strategy

Amazon’s vision is to become the world's most customer-centric company. This means looking over the easy navigation of all Amazon resources online. In addition, Amazon aims to offer a great customer experience without facing issues in using the digital platform.

If there are any issues faced by customers, data scientists come to the rescue. And this can happen on a daily basis. The data-driven company requires data scientists to help run digital platforms by overseeing all generated data.

  1. Business Growth

Apart from ensuring a great customer experience, data scientists also use big data to calculate the growth of Amazon’s business and the exact strategy that can lead to their high performance.

This is done through statistical methods to come up with behavioral patterns of customers, popular services, forecasting business trends, and rising ever-changing customer demands.

What Does an Amazon Data Scientist Do? [Roles and Responsibilities]

Amazon data scientists play the role of engaging with the academic community by either publishing research or teaching. Additionally, they also use a, ‘working backward’ method that improves how the Amazon community lives and works.

1.     Balancing between business and tech solutions

The role of a data scientist at Amazon is to provide a balance between both technical and business skills. In order to perform the best data analysis, you must keep in mind the customer-centric nature of the business.

2.     Create, execute and improve algorithms

One of the roles of data scientists at Amazon is to create and improve algorithms that can help run the business models in a way that achieves the maximum number of goals. This comes under the field of machine learning or ML which is discussed further in detail.

  • Build ML Models

By easily constructing ML models at scale and preparing them for training, Amazon SageMaker provides everything that is needed to categorize training data, evaluate it, share notebooks, and practice built-in algorithms and frameworks.

  • Train ML Models

In addition, Amazon SageMaker aids in training ML models as it gives you everything you need for training, tuning, and debugging models for later; achieving the highest accuracy.

  • Use ML Models

On a fully controlled infrastructure, Amazon SageMaker also helps you in the use of ML models in production with constant observance so that you can maintain high quality.

Don't forget that traditional ML development is a hard, pricey, and repetitive process. In fact, it is made even more complex because of the absence of proper tools for the whole ML workflow to build, train, and deploy models at scale.

However, Amazon SageMaker makes the pathway easier and cheaper by simultaneously maintaining high accuracy.

3.     Working Well in a Team

Most importantly, data scientists at Amazon are team players. Each team works on different projects. Regardless of your skills or qualifications, you should always remember you are not alone in what you do. And, of course, you should make an effort to conform to group dynamics.

The teams can work on different business sections or departments such as Amazon Web Services (AWS) and North America Supply Chain Organization (NASCO).

Is AWS Used in Data Science? Selling Partner Support Organization

Let’s give you a better example of the specific tasks that you’ll be doing in different sections of the company. The Selling Partner Support Organization under Amazon aims to help the 3rd party Sellers of any size to start the business they desire. Thus, merchants are fully supported to sell on a worldwide scale via the Amazon platform.

To give you a better idea, the functions of Selling Partner Support are:

  • To analyze a seller's needs beforehand
  • Establish new self-help tools
  • Advise solutions to cater to customers’ concerns

Hence, the Selling Partner Support is an interface between Amazon and its 3rd party sellers.

What Does an Amazon Data Scientist Do? -Types of Data Scientists

  1. Data Analytics/Business Intelligence

Business intelligence is all about the creation of forecasts and spotting new opportunities in business strategy. In addition, it involves providing deep, informed insights related to business.  These tasks call for skills in data warehousing and data science tools such as Tableau.

  1. Machine Learning Research Scientists

The main job of research scientists is to focus on applying artificial intelligence to the company’s framework. Following are the examples of applying AI to:

  • Human linguistics
  • Deep learning
  • Recommending products to a particular user

This is all done for the sake of improving the user experience.

Researchers that are fit for this role usually have a lot of knowledge of artificial intelligence and are largely famous experts and PhDs in relevant fields.

  1. Data/Applied Scientists

Applied scientists are dedicated to working with a global data set. Such a data set is usually large and applied scientists focus on developing real-world simulations; which basically provides a model of what is to be expected in terms of data figures. These simulations have the sole purpose of experimenting with data sets.

Scientists focus on applying conceptual and complex algorithms to the current system to make Amazon a more efficient and state-of-the-art platform.

  1. Data Engineer

The engineering department is responsible for developing new technology that would be applicable to Amazon services. Engineering usually refers specifically to software engineering, and personnel requirements include skills like expertise in JAVA or C++.

These languages are object-oriented, meaning that they will focus on organizing data sets and not on the logic of the program itself.

Amazon Data Scientist Skills Checklist

An eligible candidate applying for the position of data scientist at Amazon should have proficiency in the following skills in order to make the cut:

  • Statistical software packages
  • R, Stata
  • Python
  • SQL
  • C++
  • Java
  • Other functional programming languages
  • Mathematic skills
  • Excellent verbal and written communication skills
  • Ability to effectively convey solutions to data problems to both business teams and research scientists

Don't have the required skill set? Nothing to worry about! There are thousands of amazing online tutorials and data science certifications that can help you get through any course from data visualization, all the way to computer vision.

Amazon Data Scientist Qualifications and Experience

Amazon prefers candidates with a PhD. in fields such as machine learning, data science, statistics, and other related fields.

They require a minimum of 4 years of experience and a Masters’ degree in the relevant field for a base-level eligibility screening.

The company accepts candidates with masters’ degrees in fields such as :

  • Statistics
  • Quantitative Finance
  • Economics
  • Computer Science
  • Mathematics
  • Physics
  • Computational Biology
  • Operational Research

However, equivalent practical experience in these fields is also acceptable depending on the ability of the candidate. Amazon gives a lot of importance to work experience in:

  • 2+ year's work experience
  • 4+ years for Senior Data Scientist
  • Machine learning techniques for data analysis
  • Data extraction, analysis, and communication
  • Design and application of algorithms in machine learning customized for different business types
  • Testing algorithms on big data

With the correct amount of experience, you could opt for the position of a Sr. data scientist too. In fact, if you're a software engineer or have experience in software development, then you're already 2/3 of the way through to becoming a data scientist.

Interview process

Amazon's interview process is similar to other tech companies, especially their data scientist interview questions. As Amazon does not conduct take-home challenges, therefore, an initial phone interview is conducted by a recruiter or hiring manager. This is followed by a technical phone screen interview, and lastly a physical interview that takes place informally at lunch. This overall process happens in five stages.

Initial Screen

The initial phone interview is a resume-based interview usually taken by a recruiter or hiring manager at Amazon. At this point, your resume or LinkedIn profile is read through and the job position is described.

Furthermore, the hiring manager explains their team role and on which hierarchy level their department falls under in the organization. At this stage, you don't need to worry about intimidating interview questions.

The Technical Screen

The initial phone interview is followed by a technical screening. Here, you are expected to answer at least two coding questions regarding SQL and algorithm coding.

This is done over a shared code editor. Moreover, staying alert is vital, as there is an approach section that has questions on the steps and processes involved in the solution and its reasoning.

Furthermore, there is a machine learning concept question. It is quite theoretical, so going through the ML concepts would suffice.

Once you’ve managed to clear the technical phone screen successfully, the next step would be an onsite interview. The questioning process will be from A/B testing, machine learning concepts, exploratory data analysis, and coding.

At this stage, you will have 5 to 6, one on one interviews with two people; a manager and a junior data scientist. This process might take a maximum of 6 hours, covering the following:

  1. A behavioral interview to know the culture-fit
  2. A technical interview to assess data analysis and A/B testing
  3. SQL-based interview with a data scientist
  4. Algorithms and optimizations
  5. Case study interview on machine learning and modeling

All these stages will gauge your knowledge of Amazon leadership principles, critical thinking, and problem-solving skills.

Succeeding at Amazon

Data science problems can be solved by two different methods:

  1. Data is provided and you have to identify its best use
  2. Figure out the problem first and then search the data needed to answer that problem

There must be several different projects for improving data science skills and concepts you might have worked on before you joined Amazon. Once you’ve started working at Amazon, you will realize that one big project from start to end should have been enough.

In order to gain relevant experience, you should complete one data science project from beginning to end. This will help you in the entire data science pipeline.

Therefore, carrying out numerous tasks is not necessary. Just a single project is enough if you complete it end-to-end. You can start off by collecting data, explaining the problem, data cleaning, constructing a model, assessing that model, etc. Doing all this shows that you are not only experienced but also well-informed on the entire data science network.

Let’s not forget the 14 leadership principles of Amazon that all employees should follow relentlessly. These principles include:

  1. Owning the company and its projects
  2. Curiosity to learn
  3. Constantly trying to improve standards
  4. Producing promising results
  5. Having the guts to disagree

In short, data scientists at Amazon have a pretty decent career path.

The Future

Data science is all about finding solutions to new intriguing business problems. Data scientists at Amazon are on the right track with the best career in their hands. This position is one that is coveted by all, and in the 21st Century has grabbed a lot of attention.

That is why the data scientist salary is among the highest, compared to other jobs in the US.

In short, you can really make an impression at Amazon and keep a high-paying, full-time job by getting on board with the relevant experience and undying love for the job.

Published in Career Resources, Job Descriptions

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