Amazon Prep Video - Applied Scientist (AS)

Inside Amazon
22 Jun 202209:05

TLDRThe video script offers a comprehensive guide for candidates preparing for an interview at Amazon, particularly for applied science roles. It emphasizes the importance of understanding the unique interview process, which includes a research presentation and multiple interview rounds focusing on coding, science breadth and depth, and problem-solving skills. Candidates are advised to practice coding, review their past projects and research, and be prepared to discuss technical concepts clearly. The STAR method is recommended for discussing past experiences, and candidates should be ready to think out loud and consider edge cases in their problem-solving approach.

Takeaways

  • 🎉 Congratulations on advancing to the interview stage at Amazon, where the process differs from traditional interviews.
  • 📝 Spend ample time preparing, detailing past projects and hypothesizing situations to demonstrate your thought process and decision-making.
  • ❓ Ask clarifying questions during the interview to avoid assumptions and to ensure a deep understanding of the problem at hand.
  • 🗣️ Articulate your thought process out loud to allow interviewers to assess your problem-solving and decision-making abilities.
  • 🕒 The interview structure consists of a one-hour research presentation followed by five 60-minute back-to-back interview meetings.
  • 🎯 Focus on sharing detailed insights about your academic research or recent projects during the presentation, being prepared for thorough questioning.
  • 💻 Coding interviews will assess Python or preferred language skills, focusing on data structures, algorithms, and problem-solving.
  • 🌟 Use LiveCode, Amazon's online platform, for coding interviews, but note that you cannot run or compile code during the session.
  • 🧠 Prepare for functional interviews by reviewing your experience, research, and projects, and be ready to discuss them in depth.
  • 🔍 During science application and problem-solving sections, expect to handle case studies or real-world Amazon problems from start to finish.
  • 📈 Emphasize clear communication of technical concepts, requirement gathering, problem breakdown, and the ability to discuss various use cases and solutions.

Q & A

  • What is the main purpose of the video?

    -The main purpose of the video is to help candidates prepare for their interviews at Amazon, providing insights into the interview process and tips for success.

  • How is interviewing at Amazon different from other interviews?

    -Interviewing at Amazon is different because it involves a unique structure and focuses on the candidate's ability to think critically, solve problems, and communicate effectively, rather than just relying on past experiences.

  • What should candidates expect during the interview process?

    -Candidates should expect a two-part interview process consisting of a one-hour research presentation and five rounds of back-to-back interview meetings, each lasting 60 minutes.

  • What should candidates include in their research presentation?

    -Candidates should share their academic research or recent projects that highlight their domain experience, providing details and being prepared for questions from the panel.

  • How should candidates prepare for the functional interview?

    -Candidates should review their past projects and experiences, focusing on the technical and functional aspects of the role, and be ready to discuss their thought processes and decision-making.

  • What coding skills will candidates be assessed on?

    -Candidates will be assessed on their ability to write code in Python or another language of their choice, focusing on data structures, algorithms, and problem-solving skills.

  • What is LiveCode, and how is it used in the coding interview?

    -LiveCode is Amazon's online coding platform that supports multiple programming languages. It is used to conduct the coding interview, but candidates cannot run or compile their code during the exercise.

  • What are some tips for the coding interview?

    -Candidates should practice coding challenges, ask clarifying questions, think out loud, consider edge cases, and discuss time and space complexity, as well as the tradeoffs of different algorithm approaches.

  • What topics will candidates be asked about in terms of science breadth and depth?

    -Candidates will be asked about their experience and research in machine learning, mathematics, statistics, and other relevant fields, as well as the methods and strategies used in their past projects.

  • How should candidates approach science application and problem-solving questions?

    -Candidates should be prepared to discuss end-to-end project management, including requirement gathering, data collection, modeling, deployment, and performance tuning, while considering key technical decisions and potential edge cases.

  • What is the STAR method mentioned in the video, and when should candidates use it?

    -The STAR method stands for Situation, Task, Action, and Result. Candidates should use it to organize their answers when discussing past projects, highlighting the challenges, actions, and outcomes of their solutions.

Outlines

00:00

📝 Introduction and Interview Process Overview

The video begins with an introduction to Shreya, an applied science manager at Amazon, who congratulates the viewer on progressing in the interview process. She emphasizes that Amazon's interviewing process is unique and this video, along with other materials, is designed to help prepare for it. The importance of spending time preparing for the interview is highlighted, including discussing past projects and approaching hypothetical situations thoughtfully. The viewer is encouraged to ask clarifying questions during the interview and to think out loud to allow the interviewer to understand their thought process. The structure of the interview is then outlined, consisting of a one-hour research presentation followed by five rounds of back-to-back interview meetings, each lasting 60 minutes. The presentation should include sharing of academic research or recent projects, with a focus on clear communication of technical concepts.

05:00

💻 Technical and Functional Interview Preparation

This paragraph delves into the specifics of how to prepare for the functional and technical aspects of the Amazon interview. It begins with coding preparation, noting that interviewers will assess the ability to write code in Python or another language of choice. The focus will be on data structures and algorithms, with topics such as sorting, searching, merging, recursion, loops, function definitions, and linear time complexity. The LiveCode platform, used by Amazon for coding interviews, is introduced, and tips are provided for success, including practicing on platforms like HackerRank or LeetCode, asking clarifying questions, and considering edge cases and time/space complexity. The paragraph then moves on to discuss the expectations for science breadth and depth, where interviewers will inquire about fundamental knowledge in machine learning, mathematics, and statistics, as well as any relevant experience in areas like deep learning or natural language processing. For science depth, the interviewer will probe details about methods and strategies used in past projects. The importance of reviewing common data science and machine learning questions, familiarizing oneself with one's resume, and being prepared for in-depth discussions is stressed. The paragraph concludes with a discussion on science application, where the viewer can expect to receive a case study or scenario-based question to drive a project from end-to-end, including requirement gathering, data collection, modeling, deployment, and performance tuning. The viewer is advised to ask clarifying questions, consider all use cases, and defend their solutions with reasoning. The importance of a data-driven, efficient solution that considers customer experience and includes feature engineering and data/model selection is highlighted.

Mindmap

Keywords

💡Applied Science Manager

An Applied Science Manager is a professional role at Amazon focused on overseeing the application of scientific methods and technologies to solve complex business problems. In the context of the video, this role involves managing and leading projects that integrate scientific research with practical applications, as Shreya, the speaker, holds this position and provides insights into the interview process for candidates applying for similar roles.

💡Interview Process

The interview process refers to the series of assessments and conversations conducted by a company to evaluate a candidate's suitability for a particular role. In the video, the interview process at Amazon is highlighted as distinct from other companies, emphasizing the importance of preparation and understanding the unique structure and expectations, including a research presentation and multiple interview rounds.

💡Hypothetical Situations

Hypothetical situations are scenarios created to assess a candidate's problem-solving abilities, decision-making skills, and adaptability under simulated conditions. In the video, candidates are encouraged to practice thinking through and discussing their approach to hypothetical situations, as this will demonstrate their thought process and ability to handle real-world challenges at Amazon.

💡LiveCode

LiveCode is Amazon's proprietary online coding platform used in technical interviews to assess a candidate's programming skills. It allows candidates to write and demonstrate code, typically in Python or another language of their choice, without the ability to run or compile the code during the interview. This platform evaluates the candidate's ability to write clean, efficient, and correct code under time constraints.

💡Science Breadth and Depth

Science breadth and depth refer to a candidate's range of knowledge and expertise in scientific fields, as well as their ability to delve into the intricacies of specific topics. In the context of the video, this concept is used to describe the面试官's assessment of a candidate's foundational knowledge in machine learning, mathematics, and statistics, as well as their in-depth understanding and application of these concepts in their work.

💡Machine Learning Frameworks

Machine learning frameworks are software libraries or tools that provide an environment for implementing, training, and deploying machine learning models. They offer a set of pre-built functions and algorithms that simplify the process of developing complex machine learning applications. In the video, these frameworks are part of the foundational knowledge that interviewers may assess to understand a candidate's expertise and experience in applying scientific methods to real-world problems.

💡STAR Method

The STAR method is a structured technique used to answer behavioral interview questions effectively. It stands for Situation, Task, Action, and Result, and guides the candidate to provide a concise yet comprehensive response that highlights their problem-solving skills and past achievements. In the video, the STAR method is recommended for organizing answers to interview questions, allowing candidates to clearly articulate their experiences and the impact of their actions.

💡Edge Cases

Edge cases are scenarios or conditions that lie at the extremities of a problem's parameters, often revealing weaknesses or areas that need special consideration. In the context of the video, edge cases are important to consider during the interview as they demonstrate a candidate's thorough understanding and ability to anticipate potential issues with their solutions.

💡Tradeoffs

Tradeoffs refer to the compromises or balance between different aspects of a solution, often involving a sacrifice in one area to achieve benefits in another. In the video, tradeoffs are a critical aspect of decision-making during the interview process, as candidates are expected to weigh various factors, such as simplicity versus complexity, efficiency, and the potential impact on customers, when proposing solutions.

💡Science Application

Science application involves the practical use of scientific knowledge and methods to solve real-world problems. In the video, this concept is central to the interview process, with candidates expected to demonstrate their ability to apply scientific principles to business challenges, from requirement gathering and data collection to modeling and deployment.

Highlights

Interview process at Amazon is unique and requires specific preparation.

Candidates should spend time preparing for the interview, including details about past projects and hypothetical situations.

Interviewers encourage candidates to ask clarifying questions and avoid reliance on assumptions.

The interview structure consists of a one-hour research presentation followed by five rounds of back-to-back interview meetings.

Candidates should be ready to share academic research or recent projects that highlight domain experience.

Clear communication of technical concepts is expected during the presentation.

Coding skills will be assessed using LiveCode, Amazon's online coding platform.

Interviewers will ask coding questions related to data structures, algorithms, and linear time complexity.

Candidates should practice coding challenges on platforms like HackerRank or LeetCode.

Science breadth and depth will be evaluated, including questions on ML, mathematics, statistics, and various machine learning algorithms.

Interviewers will probe details about methods and strategies used in past research or industry projects.

Candidates should review common data science and machine learning questions and be ready to discuss their technical approaches in detail.

The STAR method (situation, task, action, result) should be used to organize answers about past projects.

Science application questions involve real-world Amazon problems and require a comprehensive project approach from end-to-end.

Candidates must gather requirements, break down the problem, and discuss solutions without making assumptions.

Interviewers will ask probing questions about machine learning and data science methodologies.

Candidates should be prepared to discuss feature engineering, data and model selections, and edge cases.

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