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职位Amazon

Applied Scientist, MAPLE - Recommender System

Amazon

Applied Scientist, MAPLE - Recommender System

Amazon

Bengaluru, KA, IND

·

On-site

·

Full-time

·

1mo ago

必备技能

Python

Java

Linux

Machine Learning

Are you excited by the idea of developing personalized experiences for Amazon customers as they shop? Are you looking for new challenges and to solve hard science problems while applying state-of-the-art recommendation system modeling and GenAI techniques? Join us and you'll help millions of customers make informed purchase decisions while also advancing the state of Amazon's science by publishing research!

  • Key job responsibilities
  • Participate in the design, development, evaluation, deployment and updating of data-driven models for shopping personalization.
  • Develop and test new signals for improving recommendation models
  • Use supervised and uplift learning algorithms to improve customer experience
  • Contribute to production code and science tooling
  • Design A/B tests and conduct statistical analysis on their results
  • Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers
  • Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area
  • Present and publish science research internally and externally, contributing to Amazon's science community
  • Mentor junior engineers and scientists.

About the team
Our team's mission is to surface the right payments-related recommendations to customers at the right time, helping create a rewarding and successful shopping experience for Amazon's customers. Our team's culture is highly collaborative, with an emphasis on supporting each other and learning from one another. We dedicate time each week to focus on personal development and expanding our knowledge as a team. We also highly value having a big impact, both for Amazon's business and for our customers.

Basic Qualifications

  • 3+ years of building models for business application experience
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience programming in Java, C++, Python or related language
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred Qualifications

  • Experience using Unix/Linux
  • Experience in professional software development

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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关于Amazon

Amazon

Amazon

Public

Amazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.

10,001+

员工数

Seattle

总部位置

$1.5T

企业估值

评价

2.9

10条评价

工作生活平衡

2.8

薪酬

3.7

企业文化

2.5

职业发展

2.3

管理层

2.1

35%

推荐给朋友

优点

Good pay and compensation

Strong benefits package

Flexible scheduling options

缺点

Poor management and leadership

Limited growth and promotion opportunities

High stress and demanding work environment

薪资范围

4个数据点

L2

L3

L4

L5

L6

M3

M4

M5

M6

L2 · Sales L2

0份报告

$183,390

年薪总额

基本工资

$73,356

股票

$91,695

奖金

$18,339

$128,373

$238,407

面试经验

10次面试

难度

3.7

/ 5

时长

21-35周

录用率

20%

体验

正面 10%

中性 10%

负面 80%

面试流程

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Onsite/Virtual Loop

6

Team Matching

7

Offer

常见问题

Coding/Algorithm

System Design

Behavioral/STAR

Leadership Principles

Technical Knowledge