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Amazon Data Scientist Interview: A Complete Guide
Data Science

Amazon Data Scientist Interview: A Complete Guide

5 minute read | July 8, 2020
Sakshi Gupta

Written by:
Sakshi Gupta

Ready to launch your career?

Data science roles at Amazon are highly competitive and difficult to land. Fortunately, enough people have successfully gone through the Amazon data scientist interview process to share their experiences and offer valuable advice.

If you’ve graduated from a data science degree or bootcamp, put together an impressive portfolio, polished your programming and quantitative analytics skills, and have scored a coveted interview with an Amazon recruiter or hiring manager, you might understandably be nervous about what comes next.

Interviewing for any role can be an intimidating experience, but the best way to ensure success is to be prepared for what you’ll be asked and to know what the company is looking for. Current and former Amazon data scientists share their own interviewing experiences below. Read on to learn more about what it’s like to interview for a data science role at Amazon.

What does a data scientist at Amazon do?

Data scientists at Amazon fall into four broad categories, each allowing them to specialize and apply their analytical skills to different product and service areas.

  • Data analytics

Data scientists who specialize in this area typically have a focus on business intelligence. This role requires them to create dashboards, forecast trends, devise strategic solutions to business challenges, and present data-backed findings to leadership and other stakeholders inside the company in an accessible way. As a customer-centric company that’s driven by data, being able to wrangle and input large amounts of data to forecast how people will shop and use its platforms and services is a top concern for Amazon.

  • Machine learning

Machine learning specialists at Amazon usually require a Ph.D. or graduate qualifications in deep learning, natural language processing, or computer vision. Data and research scientists in this role are on the cutting edge of research and assist the company in developing new algorithmic models that power Amazon’s Alexa, its streaming services, Amazon Web Services, and other parts of the business.

  • General data science

Amazon hires many data science generalists to write optimization algorithms, build experiments with AB testing, run algorithms and models to find actionable insights, and make meaningful recommendations and offer feedback to leadership based on their findings.

  • Data engineering

Data analysts in this area are focused on building out data pipelines. Their work often overlaps with that of machine learning engineers.

What skills does a data scientist at Amazon require?

In addition to being proficient in programming languages such as SQL and Python and understanding statistics, Amazon expects its data scientists—whether they’re generalists or machine learning specialists— to be experienced with the full data science pipeline, such as defining a problem, data cleaning, developing a model, evaluating it, and analyzing the findings.

Aashish Jain, a Springboard alumnus who started at Amazon as a data scientist before advancing to the role of a research scientist, said that even though he spends most of his time reading the latest research and developing algorithmic models, he still finds himself using those foundational data science skills in his day-to-day work.

“I mainly work with building different machine learning models, and that involves reading papers, finding out which algorithm works based on my problem, and implementing it using Python,” Jain said. “But it’s not just model building—it’s evaluating. There’s data analysis and data cleaning. It’s the whole data science pipeline.”

It’s not enough to simply be technically proficient, though, according to current and former Amazon employees. The company has a list of fourteen leadership principles that it expects its employees to strive toward, some of which include: customer obsession, think big, dive deep, and deliver results.

In practice, this translates to skills such as having the ability to focus and pay attention to detail, being able to meet deadlines, knowing what kinds of questions need to be asked and answered, and approaching projects with the company’s mission and ambitions in mind.

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What is the interview process for a data scientist at Amazon like?

Amazon’s interview process for data scientists involves a series of phone interviews, followed by onsite interviews and technical challenges.

  • The phone screener. Similar to the interview process at Google and Facebook, data scientists who have been through Amazon’s interview process describe the first round of phone interviews that judge a candidate’s suitability for the role. Initial rounds include general HR questions about an applicant’s background, experience, and why they want to work for Amazon. There is then a technical screen, during which the applicant will be asked to explain data science concepts to show that they have the foundational technical knowledge to do the job. According to Glassdoor, applicants have in the past been asked questions such as, “Explain p-value,” “Explain bias-variance tradeoff,” “What is the difference between bagging and boosting?” and “Explain Bayes’ Theorem.” Applicants can also expect to solve SQL or Python algorithm coding questions during this stage in the interview process.
  • The behavioral questions. During both the phone interview and the more grueling onsite interview—the latter of which involves a loop with around five or six hiring managers, data scientists, and members of leadership—applicants will be asked data science interview questions where they will be expected to demonstrate the Amazon leadership principles. For example, a hiring manager might ask an applicant to discuss former projects, talk about a time they have failed, or explain occasions when they made trade-offs. According to people who have experience with the interview process, these questions are designed to invite the applicant to connect their response to one of Amazon’s leadership principles, so it’s worth crafting a story around each principle to share during the interview.

What happens after the interview process?

Depending on the role an applicant is applying for, the data science interview process at Amazon can involve additional technical challenges, such as solving coding problems on a whiteboard, or answering questions on machine learning and predictive modeling.The nature of the challenges and additional questions are determined by the needs of the hiring team, the responsibilities of the particular data science role, and the candidate’s level of experience.“Different teams have different requirements,” said Jain, who added that hiring managers at Amazon typically assess job seekers based on both prior experience and the ability to demonstrate that they have the foundational skills and mindset to succeed at Amazon.

“They look at how you would work if you were given a problem. They also want to know what you have done in the past because that tells them about your problem-solving skills. They dig into both.”

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.