During your interviews for the Applied Scientist position, you should anticipate a range of questions that assess both your technical and behavioral competencies. The questions outlined below are representative of what candidates commonly report, although they may vary by team and focus area. The goal is not to memorize answers but to illustrate the types of challenges you might face.

Technical / Domain Questions

This category evaluates your knowledge in machine learning, statistics, and data processing.

• Explain the difference between precision and recall. Why are both important?
• Describe how you would evaluate the performance of a classification model.
• What are some common pitfalls when working with imbalanced datasets?
• Discuss a machine learning project you've worked on and the challenges you faced.
• How do you approach feature selection and engineering in a dataset?

System Design / Architecture

Expect to discuss how you would design systems or data pipelines for specific applications.

• How would you design an end-to-end machine learning pipeline for a recommendation system?
• Describe how you would structure a multi-modal data processing system.
• What considerations would you take into account when designing a scalable data architecture?

Behavioral / Leadership

Behavioral questions will gauge your cultural fit and leadership qualities.

• Tell me about a time when you faced a significant challenge in a project. How did you handle it?
• Describe a situation where you had to influence others to adopt your point of view.
• How do you prioritize your tasks when working on multiple projects?

Problem-Solving / Case Studies

This section tests your analytical thinking and practical application of concepts.

• Given a dataset with missing values, how would you handle it?
• Discuss your approach to troubleshooting an underperforming machine learning model.
• How would you conduct a root-cause analysis for a failure in a deployed model?

Coding / Algorithms

You may be asked to demonstrate your coding skills, particularly in algorithms relevant to data science.

• Write a function to compute the Chamfer distance between two point sets.
• Implement binary search on a sorted array and explain its time complexity.
• Solve a problem involving dynamic programming or graph traversal.

This visual timeline provides an overview of the interview stages, illustrating the balance between technical assessments and behavioral evaluations. Use it to plan your preparation strategically and to manage your energy effectively across the interview rounds.

Deep Dive into Evaluation Areas

Candidates will be assessed across multiple evaluation areas that are critical for success in the Applied Scientist role. Understanding these areas will help you tailor your preparation effectively.

Technical Expertise

Your deep understanding of machine learning principles, algorithms, and frameworks is crucial. Interviewers will evaluate your ability to apply theoretical knowledge to real-world problems. Strong performance includes:

• Ability to articulate complex concepts clearly.
• Demonstrating familiarity with recent advancements in machine learning.
• Practical experience with tools such as Python, TensorFlow, and PyTorch.

Be ready to go over:

• Core ML algorithms (e.g., regression, classification, clustering)
• Evaluation metrics and their implications
• Techniques for model optimization and hyperparameter tuning

Project Experience

Your past experiences with relevant projects will be a focal point during the interviews. Interviewers will look for:

• Depth of involvement in the projects.
• The challenges faced and how you overcame them.
• Your role in the team and contributions to project success.

Example questions or scenarios:

• Describe a project where you implemented a machine learning solution from scratch.
• Discuss the data sources you used and how you managed data quality.

Problem-Solving Skills

Your ability to think critically and approach problems systematically is vital. Interviewers will assess:

• How you dissect problems into manageable parts.
• Your approach to testing and validation.
• The creativity and efficiency of your solutions.

Be ready to go over:

• Case studies or scenarios that require analytical thinking.
• Strategies for troubleshooting and refining models.

Collaboration and Communication

Your capacity to work effectively within teams and communicate complex ideas will be evaluated. Strong candidates demonstrate:

• Ability to explain technical concepts to non-technical stakeholders.
• Experience collaborating across different teams and functions.
• Leadership qualities in guiding team discussions and decisions.

Example questions or scenarios:

• Share an experience where your communication skills helped resolve a conflict.
• How do you ensure alignment and buy-in from diverse team members?

Innovation and Adaptability

The fast-paced nature of Amazon DSP requires scientists who can innovate and adapt quickly. Assessments may focus on:

• Your willingness to learn new technologies and methodologies.
• Examples of how you’ve adapted to changes in project direction or scope.
• Contributions to innovative solutions that drive business impact.