Become an Applied Scientist at Amazon

Become an Applied Scientist at Amazon

Table of Contents

1. Introduction
2. Understanding the Interview Process at Amazon
   • Differences from previous interviews
   • Importance of preparation
   • Emphasizing past projects and problem-solving approach
   • Thinking out loud during the interview
3. Interview Structure
   • Components of the interview
   • Research presentation
   • Back-to-back interview meetings
   • Presenting academic research or recent projects
   • Clear communication and technical concepts
4. Preparing for the Functional Interview
   • Technical and functional aspects of the role
   • Coding skills and languages
   • Typical coding topics and LiveCode platform
   • Tips for the coding interview
5. Science Breadth and Depth
   • Questions about experience and research
   • Fundamental knowledge in ML, mathematics, and statistics
   • Conceptual questions and differences between algorithms
   • Relevant areas of experience, such as deep learning and NLP
   • Details about methods and strategies used in past projects
6. Science Application and Problem Solving
   • Case study and Scenario-Based questions
   • Driving a project from end-to-end
   • Gathering requirements and breaking down the problem
   • Making technical decisions and considering edge cases
   • Defending solutions and discussing machine learning methodologies
7. Conclusion
   • Recap of key points
   • Contacting the recruiter
   • Good luck in the interview preparation

Article

Understanding the Interview Process at Amazon

Moving forward in the interview process at Amazon is an exciting milestone. However, it's essential to recognize that Amazon's interview style differs from those You may have experienced in the past. In this video, we'll provide guidance and insights to help you succeed in the Amazon interview. Preparation is key in this process, and dedicating time to it will greatly increase your chances. As an applied science manager at Amazon, I'll share valuable tips to set you up for success during the interview.

When preparing for the interview, it's crucial to focus not only on what you did but also why and how you approached past projects. Amazon interviewers are interested in understanding your thought process and how you make decisions and trade-offs. They want to see your problem-solving approach and assess your ability to explain technical concepts clearly. Therefore, thinking out loud during the interview is highly encouraged.

Interview Structure

To give you a clear understanding of what to expect, let's discuss the structure of the Amazon interview. It consists of two parts: a one-hour research presentation and five rounds of back-to-back interview meetings. Each round, including the presentation, is scheduled for 60 minutes. During the research presentation, you'll have the opportunity to share your academic research or recent projects that highlight your domain experience. Make sure to walk through your research with Attention to Detail, as the panel will ask questions about each part of your presentation. Clear communication in explaining technical concepts is crucial to demonstrate your expertise.

Preparing for the Functional Interview

Now, let's dive into the specific technical and functional aspects of the role and how you can best prepare. The functional interview for applied scientists at Amazon assesses your coding skills and ability to write code in Python or another language of your choice. Typical coding topics include data structures, algorithms, sorting, searching, recursion, loops, function definitions, and time complexity.

During the coding interview, LiveCode, Amazon's online coding platform, will be used. It supports multiple programming languages, but you won't be able to run or compile your code. To prepare, it's advisable to brush up your coding skills by practicing easy to medium-level coding challenges on platforms like HackerRank or LeetCode. Remember to ask clarifying questions before jumping into the solution and think out loud as you solve the problem. Consider edge cases and talk about the trade-offs of different algorithm approaches. Interviewers appreciate simplicity, so choose the simplest solution and explain why you selected it. Don't forget to discuss testing and optimization strategies as well.

Science Breadth and Depth

In addition to coding skills, the Amazon interview will also evaluate your science breadth and depth. For science breadth, expect questions about your experience and research, touching upon fundamental knowledge in machine learning, mathematics, and statistics. Conceptual questions may arise concerning data training, machine learning frameworks, mathematical optimization, statistics, and the differences between various machine learning algorithms. If you have relevant experience in areas like deep learning, natural language processing (NLP), computer vision, operational research modeling, optimization, or robotics, be prepared for further exploration.

In terms of science depth, interviewers will Delve into the methods and strategies you've utilized in your past projects. Prepare to answer questions about your research and industry projects, highlighting the specific methods, strategies, and technical approaches you employed. They will examine your ability to think critically, analyze outliers, and handle edge cases. Additionally, expect questions about scoping science models, evaluating and monitoring their success, as well as fundamentals in your field of expertise. Familiarize yourself with common data science and machine learning questions and reflect on your technical approaches and problem-solving processes.

Science Application and Problem Solving

The science application questions dive into your ability to handle real-world challenges and drive projects from end-to-end. You might be presented with a case study or scenario-based question, simulating a problem faced at Amazon. The interviewer will observe how you Gather requirements and break down the problem. Pay attention to the fact that interviewers may intentionally present vague or ambiguous questions, emphasizing the importance of clarifying assumptions before tackling the technical aspects.

Once requirements are gathered, you can start discussing your solutions. However, remember to consider every possible use case, weigh different solutions against each other, and discuss the trade-offs involved. Interviewers value your ability to make informed decisions and defend your choices. Probing questions will examine your understanding of machine learning and data science methodologies. Align your discussion with the requirements and data you gathered, provide a technical analysis with pros and cons, and don't forget to consider edge cases and possible optimizations. Your solutions should prioritize customer experience, efficiency, and data-driven decision-making.

Conclusion

In conclusion, success in the Amazon interview process requires thorough preparation and a clear understanding of the expectations. Make sure to emphasize your problem-solving approach, think out loud, and communicate technical concepts effectively. Remember the interview structure, including the research presentation and back-to-back interviews, and ensure you are well-prepared for the coding, science breadth and depth, as well as science application and problem-solving components. Good luck with your preparation and interview!

Highlights

• Differences in the Amazon interview process.
• Importance of thorough preparation.
• Emphasizing problem-solving approach and thinking out loud.
• Structure of the Amazon interview and research presentation.
• Tips for the coding interview and LiveCode platform.
• Evaluation of science breadth and depth.
• Discussing past projects and technical approaches.
• Case study questions and driving projects from end-to-end.
• Gathering requirements and making informed technical decisions.
• Prioritizing customer experience and efficient, data-driven solutions.

FAQ

Q: How should I prepare for the Amazon interview process? A: It's crucial to spend time preparing for the interview, emphasizing your past projects and problem-solving approach. Brush up on your coding skills, practice data science and machine learning questions, and think critically about your technical approaches.

Q: Which programming language should I focus on for the coding interview? A: Applied scientists at Amazon are expected to write code in Python or another language of their choice. Choose a language you are comfortable with and ensure you can write code efficiently and accurately.

Q: What should I expect in the science breadth and depth evaluation? A: The science breadth evaluation will cover fundamental knowledge in machine learning, mathematics, and statistics. The science depth evaluation will delve into the details of the methods and strategies you've used in your past projects.

Q: How can I approach scenario-based questions in the science application section? A: For scenario-based questions, gather all the requirements, clarify any assumptions, and break down the problem before discussing your solutions. Consider all possible use cases, discuss trade-offs, and defend your choices with proper reasoning.

Q: What are the key factors to consider during the interview process? A: It's important to think out loud, communicate technical concepts clearly, provide detailed answers about past projects, and demonstrate your ability to make informed technical decisions. Customer experience, efficiency, and data-driven solutions should also be prioritized.