refresh

トレンド企業

トレンド企業

採用

求人Amazon

Applied Scientist, Personalization, Personalization Strategic Initiatives Science

Amazon

Applied Scientist, Personalization, Personalization Strategic Initiatives Science

Amazon

Tel Aviv, ISR

·

On-site

·

Full-time

·

3w ago

Are you a scientist interested in pushing the state of the art in machine learning and recommendation systems? Are you interested in working on novel ideas that can positively impact millions of customers? Do you wish you had access to large datasets and tremendous computational resources? Answer yes to any of these questions and you will be a great fit for our team at Amazon.

Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in machine learning, big data, distributed systems, and user experience design to deliver the best shopping experiences for our customers. Our team builds large-scale machine-learning solutions that delight customers with personzlized content recommendations, at the right time, with the right level of explanation.

As an Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for personalization. You will adopt or invent new machine learning and analytical techniques in the realm of recommendations and large language models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products and systems.

Please visit https://www.amazon.science for more information.

Basic Qualifications

  • PhD in CS/EE or related field, or MSc and 5+ years of applied research experience
  • Strong CS foundations (data structures and algorithms)
  • Excellent coding and design skills, proficiency with programming languages such as Java or Python
  • Several publications at top-tier peer-reviewed research conferences or journals
  • Strong communication and collaboration skills

Preferred Qualifications

  • Experience in building and launching deep learning and machine learning models for business applications
  • Solid knowledge of big data and cloud technologies (e.g., Spark, AWS, etc.)
  • Experience with information retrieval, recommender systems, natural language processing, and/or personalization algorithms
  • Publications at top Web, Machine Learning, Natural Language Processing conferences such as KDD, ICML, NeurIPS, ACL, EMNLP, etc.

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.

総閲覧数

0

応募クリック数

0

模擬応募者数

0

スクラップ

0

Amazonについて

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+

従業員数

Seattle

本社所在地

$1.5T

企業価値

レビュー

2.9

10件のレビュー

ワークライフバランス

2.8

報酬

3.7

企業文化

2.5

キャリア

2.3

経営陣

2.1

35%

友人に勧める

良い点

Good pay and compensation

Strong benefits package

Flexible scheduling options

改善点

Poor management and leadership

Limited growth and promotion opportunities

High stress and demanding work environment

給与レンジ

4件のデータ

Junior/L3

L2

L3

L4

L5

L6

M3

M4

M5

M6

Mid/L4

Principal/L7

Senior/L5

Staff/L6

Director

Junior/L3 · Data Scientist L4

0件のレポート

$181,968

年収総額

基本給

-

ストック

-

ボーナス

-

$154,672

$209,264

面接体験

10件の面接

難易度

3.7

/ 5

期間

21-35週間

内定率

20%

体験

ポジティブ 10%

普通 10%

ネガティブ 80%

面接プロセス

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Onsite/Virtual Loop

6

Team Matching

7

Offer

よくある質問

Coding/Algorithm

System Design

Behavioral/STAR

Leadership Principles

Technical Knowledge