Applied Science Manager, Personalization Team

Amazon

Earth's most customer-centric company focused on e-commerce and technology

London, UK

$150,000 - $250,000

Machine Learning

Staff Software Engineer

In-Person

5,000+ Employees

5+ years of experience

AI · E-Commerce

Description For Applied Science Manager, Personalization Team

Amazon's Personalization organization is seeking an Applied Science Manager to lead a team focused on revolutionizing e-commerce experiences. This role combines big data, machine learning, and customer-centric innovation to shape the future of personalized shopping. You'll be working with Amazon's extensive computational resources and e-Commerce expertise to build next-generation shopping experiences that impact hundreds of millions of customers worldwide.

The position involves leading a team responsible for developing personalization solutions for health & personal care, groceries, and other growing product categories. You'll oversee the entire software lifecycle, from design to implementation, while leveraging machine learning approaches to create delightful customer experiences. The role demands technical excellence, strategic thinking, and strong leadership skills.

As an Applied Science Manager, you'll be part of a high-performing organization known for its culture of rapid prototyping, rigorous experimentation, and data-driven decision-making. The team has a proven track record of revolutionary features like "Customers Who Bought Also Bought" and "Recommended for You". You'll work in an environment that values customer obsession, technical innovation, and team member growth.

The ideal candidate combines technical expertise with leadership abilities, bringing experience in machine learning, distributed systems, and large-scale production environments. You'll have the opportunity to shape the future of e-commerce while working with cutting-edge technology and leading a team of talented engineers and scientists. The role offers high visibility, meaningful impact, and the chance to solve complex technical challenges at unprecedented scale.

Last updated 3 months ago

Responsibilities For Applied Science Manager, Personalization Team

Lead and mentor a team of software engineers and scientists
Oversee all aspects of the software lifecycle
Design and implement machine learning solutions
Deliver customer-facing experiences
Work with product management to define strategy and requirements
Communicate with senior management on status, risks, and strategy
Build and maintain large-scale production systems

Requirements For Applied Science Manager, Personalization Team

Python

Java

Master's degree
Knowledge of ML, NLP, Information Retrieval and Analytics
Experience directly managing scientists or machine learning engineers
Experience programming in Java, C++, Python or related language
Experience in building machine learning models for business application
Experience in applied research

Benefits For Applied Science Manager, Personalization Team

Equal opportunity employer
Inclusive work culture
Work-life balance
Learning and growth opportunities

Amazon

Earth's most customer-centric company focused on e-commerce and technology

London, UK

$150,000 - $250,000

Machine Learning

Staff Software Engineer

In-Person

5,000+ Employees

5+ years of experience

AI · E-Commerce

Amazon

Unique Paths

Data Structures & AlgorithmsMedium

There is a robot on an m x n grid. The robot is initially located at the top-left corner (i.e., grid0). The robot tries to move to the bottom-right corner (i.e., gridm - 1). The robot can only move either down or right at any point in time. Given the two integers m and n, return the number of possible unique paths that the robot can take to reach the bottom-right corner. The test cases are generated so that the answer will be less than or equal to 2 * 109. For example, if m = 3 and n = 7, the expected output is 28. Explain your approach, provide code, and analyze the time and space complexity. Also discuss edge cases and alternative solutions using combinatorics.

Dynamic Programming

Amazon

Describe the system design of your Salesforce integration with a Java backend.

System DesignMedium

Let's discuss the system design of your existing Salesforce integration with a Java backend. Could you please elaborate on the architecture, including specific details about data flow, integration patterns, and technologies used? For example, walk me through a scenario where a new account is created in Salesforce, and how that information propagates to your Java backend system. What mechanisms are in place for real-time updates versus batch processing? How do you handle data transformations and ensure data integrity across both systems? What are the key considerations for scalability, security, and fault tolerance in your design? Finally, describe the monitoring and alerting mechanisms you have implemented to ensure the integration is running smoothly and to quickly identify and address any issues that may arise. Provide details on the error handling strategies employed to prevent data loss or corruption in the event of failures.

Graphs

Database Problems

Dynamic Programming

Arrays

Amazon

Tell me about a project that you're most proud of

Behavioral

Tell me about a project that you're most proud of. To help me understand the scope and impact, please provide specific details about the project, including: The problem: What challenge were you trying to solve, and why was it important? Your role: What specific responsibilities did you have on the project? What were your key contributions? The technical details: What technologies, tools, or techniques did you use? For example, did you work with a specific programming language, framework, or database? The outcome: What were the results of the project? Did you meet the initial goals? Did you encounter any unexpected benefits or challenges? What did you learn?: Looking back, what would you do differently? What skills or knowledge did you gain from the experience? Did you face any setbacks, and how did you overcome them? For instance, if you worked on a project to improve the performance of a web application, you might discuss the specific performance bottlenecks you identified, the optimization techniques you implemented (e.g., caching, code optimization), and the resulting improvement in response time or throughput. Or, if you designed a new feature for a mobile app, you could describe the user research you conducted, the design process you followed, and the metrics you used to measure the feature's success. Be prepared to discuss technical challenges you faced and how you addressed them.

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