Everything You Might Want to Know About Being a Data Scientist at Amazon

Amazon is one of the most popular employers in the tech industry, and it’s no wonder why: Amazon is the largest internet company in the world, with more than 1 million employees and hundreds of millions of customers.

This is everything you might want to know about being a data scientist at Amazon so that you can get started with your Amazon career.

Amazon careers: Data roles available

Data scientists and analysts work on some of the most cutting-edge research in artificial intelligence and machine learning to create forecasts and optimize algorithms to power Amazon’s “customer-obsessed” business model.

Some of the different data roles at Amazon include:

1. Data scientist
2. Data engineer
3. Business analyst

Amazon data scientist: What it takes to get hired

Data scientists generally need to have four or more years of industry experience working as a data scientist or in a similar role and professional experience modeling and analyzing large data data sets.

Experience in writing and speaking about technical concepts, including data-driven presentations, and with cloud computing and big data tools are also required. Amazon also expects data scientist candidates to have experience developing strategic, baseline data modeling processes.

Some data scientists at Amazon have a master’s degree or PhD in a highly quantitative field.

Amazon data engineer: What it takes to get hired

Data engineers typically need to have five or more years of industry experience as a data engineer or in a similar role and work experience with extract, transform and load data integration, data modeling and data architecture. Experience working with Amazon Web Services big data technologies is also often required.

Some data engineers have seven or more years of working experience as a business analyst, data analyst or in a statistical analysis role before joining Amazon.

Amazon business analyst: What it takes to get hired

Business analysts usually have three or more years of experience as a business analyst, data analyst or in a statistical analysis role and a bachelor’s degree or similar experience in business, marketing, engineering, math or a related field.

Candidates have experience making business recommendations and influencing stakeholders and communicating effectively with both business and technical teams with clear, thoughtful and comprehensive analyses.

Some business analysts have five or more years of experience before getting hired at Amazon.

Types of data science positions at Amazon

Some common data science positions include working in business and marketing, data engineering and machine learning.

Business and marketing data science roles at Amazon

A data scientist at Amazon in business and marketing will:

• Analyze large amounts of data from different parts of the supply chain and their associated business functions
• Provide actionable measurement solutions and media recommendations that help advertisers understand how media drives customer action
• Optimize complex trade-offs between customer experience, inventory costs, fulfillment costs and fulfillment center capacity

Data engineering data science roles at Amazon

A data scientist at Amazon in data engineering will:

• Design and deliver big data architectures for experimental and production consumption between scientists and software engineering
• Create automated alarming and dashboards to monitor data integrity
• Develop the end-to-end automation of data pipelines, making datasets readily consumable by visualization tools and notification systems

Machine learning data science roles at Amazon

An Amazon data scientist working in machine learning will:

• Design and implement scalable and reliable approaches to support or automate decision making throughout the business
• Analyze data for trends and input validity, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies
• Develop new machine learning solutions and apply them to data problems at scale

What is the Amazon data science job interview like?

Amazon has a fairly standard and extensive job interview process for data scientists. The process includes an initial phone screening, hiring manager job interview, and a full-day on-site job interview.

1. Initial phone screen at Amazon

The initial phone-screening job interview is a short half-hour conversation where the recruiter goes over your resume and experiences, and then briefly explains the role, team, and team’s relative position within the company.

2. Technical job interview at Amazon

The technical job interview is more in-depth about your data experience and technical expertise. It includes questions about statistics, coding, algorithms, and product design, with the data science coding questions being done over a shared code editor.

3. Full-day on-site job interview at Amazon

The Amazon data science on-site job interview includes five interviews with a lunch break in between. The job interviews are a mix of behavioral questions and technical problems.

What questions do they ask during an Amazon data science job interview?

Amazon data science job interviews always include behavioral questions about your background and professional experience. They are often variations of “Tell me about a time where…”

• Where do you see yourself in five years?
• Describe a time when you had a difference of opinion with your colleagues.
• Tell me about a time when you had to work incomplete data or information.

You will also be asked questions to gauge your interest in Amazon and your knowledge of Amazon as a business.

• How would you measure the impact of a business initiative?
• How would you describe the value proposition of Amazon Web Services to the CIO of a company?
• What would you change on the Amazon website?

Some common data analysis and coding problems during the on-site job interview include:

• Find the customer with the highest total order cost between a set of dates. Output their first name, the total cost of their items and the date.
• What is the difference between a linked list and an array?
• How do you interpret the coefficient in logistic regression?
• How does a neural network with one layer and one input and output compare to logistic regression?
• If you have a customer and you know where they live, their income, gender and profession, how would you define a machine learning algorithm that predicts whether they will “buy today” or “not buy today?”
• How can you tell that your model is working?

The bottom line

Amazon takes its 14 leadership principles very seriously. Take some time to review and memorize them so that you’re ready to showcase you exhibit these qualities. It’s also important to explain your thought process during the coding challenges, as the interviewer will want to know how you approach data and problem-solving.

This article was written by Nathan Rosidi for HackerNoon and was lightly edited and published with permission.