
Work hard. Have fun. Make history.
Amazon Science Fellow, JCI Science and Data
日本の大学で機械学習や関連領域の研究に従事している学生の皆様に向けたフェローシッププログラムのご案内です。Amazon JapanのRetail Scienceチームでは、何百万人もの顧客にインパクトを与える価値あるテクノロジーに繋がるような、新しいプロトタイプやコンセプトを開発するプロジェクトに従事していただく学生を募集しています。プログラムは1ヶ月から3ヶ月の短期間のプロジェクトになります。
プロジェクトの対象となるテーマには、自然言語処理、表現学習、レコメンデーションシステム、因果推論といった領域が含まれますが、これらに限定されるわけではありません。プロジェクトは、チームのシニアサイエンティスト1名または複数名のガイダンスのもとで定義、遂行され、プロジェクト中は他のサイエンティストもメンターとしてフォローします。
学生の皆様が新しいモデルを考案したり、新しいテクノロジーを活用し実験する時間を最大化できるようにすることが目標です。そのため、プロジェクトではエンジニアリングやスケーリングよりも、プロトタイピングを行い具体的に概念実証を行うことに集中します。
また、Amazonでは論文出版も推奨しています。従事した研究開発活動の成果物として出版される論文には著者として参加することになります。
フェローシッププログラムは目黒の東京オフィスで、他のチームと一緒に行われます。Amazonは、プログラム期間中に必要なIT機器(ラップトップなど)、給与と通勤費を支給します。
Are you a current PhD student enrolled in a Japanese university researching Statistics, Machine Learning, Economics, or a related discipline? The Japan Retail Science team is looking for Fellows for short term (1-3 months) projects to develop new prototypes and concepts that can then be translated into meaningful technologies impacting millions of customers.
In this position, you will be assigned a project to carry out from areas including but not limited to natural language processing, representation learning, recommender systems, or causal inference. The project will be defined and carried out under the supervision of one or more of our senior scientists, and you will be assigned another scientist as a mentor to follow you during the project.
Our goal is to maximize the time you spend on inventing new models and experimenting with new techniques, so the work will concentrate on prototyping and creating a tangible proof of concept, rather than engineering and scaling. Amazon encourages publications, and you will be included as an author of any published manuscript.
The fellowship will be carried out from our Tokyo office in Meguro together with the rest of the team. Amazon will provide the necessary IT equipment (laptop, etc.) for the duration of the fellowship, a salary, and commuting expenses.
Key job responsibilities
このフェローシップは、顧客のデジタルツインの作成に関するものです。私たちは、過去の取引データから販売業者の顧客のデジタルレプリカ(実際の顧客行動に統計的に忠実な合成エージェント)を訓練することを提案します。これらは2つのモード で使用できます。シミュレーションモードでは、数千のツインをインスタンス化し、仮想シナリオ(価格変更、新製品発売、プロモーション戦略)を通じてモンテカルロ方式で実行し、コストのかかる実世界の実験にコミットする前に、信頼区間を持つ集計結果を予測します。対話型フォーカスグループモードでは、各ツインはLLMに基づく自然言語ペルソナによってサポートされ、会話形式でクエリを実行できます。例えば、代表的な顧客になぜ離脱したのか、またはより頻繁に購入するために何が必要かを尋ねることができ、その回答は推測ではなく、その顧客の実際の行動プロファイルと履歴に基づいています。
This fellowship is about the creation of digital twins for customers. We propose training digital replicas of a vendor's customers from historical transaction data — synthetic agents that are statistically faithful to real customer behavior and can be used in two modes. In simulation mode, we instantiate thousands of twins and run them through hypothetical scenarios (pricing changes, new product launches, promotion strategies) in a Monte Carlo fashion to project aggregate outcomes with confidence intervals before committing to costly real-world experiments. In interrogation/focus group mode, each twin is backed by an LLM-grounded natural language persona and can be queried conversationally — for example, asking a representative customer why they churned or what would make them buy more frequently — with responses grounded in that customer's actual behavioral profile and history rather than speculation.
A day in the life
チームの多くのメンバーは、午前9時くらいから10時半くらいまでの間に仕事を始め、夕方6時から7時には仕事を終えています。出席が必要なミーティングに参加していれば、勤務時間は自由に決められます。
パートタイムを希望する場合、勤務時間数は採用担当者とともに決定します。フルタイムの場合、労働時間は通常の契約通り週40時間となります。
The majority of the team starts working between 9 and 10.30am until 18-19. You will have complete flexibility to determine your working hours as long as you are present for the meetings where your attendance is required.
Number of working hours will be agreed together with the hiring manager in case you want to pursue the Fellowship part-time. In case of full-time, working hours will be 40/week as per a standard contract.
About the team
私たちのチームは、日本および世界のすべてのAmazonのベンダー企業に提供されるソリューションを支える製品を発明し、開発しています。私たちは、プロダクトマネージャーやビジネス関係者と協力し、科学的なモデルを開発し、インパクトのあるアプリケーションに繋げることで、Amazonのベンダー企業がより速く成長し、顧客により良いサービスを提供できるようにします。
私たちは、科学者同士のコラボレーションが重要であり、孤立した状態で仕事をしても、幸せなチームにはならないと考えています。私たちは、科学者が専門性を高め、最先端の技術についていけるよう、社内の仕組みを通じて継続的に学ぶことに重きを置い ています。私たちの目標は、世界中のAmazonのベンダーソリューションの主要なサイエンスチームとなることです。
Our team invents and develops products powering the solutions offered to all Amazon vendors, in Japan and worldwide.
We interact with Product Managers and Business stakeholders to develop rigorous science models that are linked to impactful applications helping Amazon vendors grow faster and better serving their customers.
We believe that collaboration between scientists is paramount, and working in isolation does not lead to a happy team. We place strong emphasis on continuous learning through internal mechanisms for our scientists to keep on growing their expertise and keep up with the state of the art. Our goal is to be primary science team for vendor solutions in Amazon, worldwide.
Basic Qualifications
- Experience programming in Java, C++, Python or related language
- Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
- Are enrolled in a Bachelor's degree or above in Engineering, Computer Science, Machine Learning, Operations Research, Statistics, or related fields
Preferred Qualifications
- Experience implementing algorithms using both toolkits and self-developed code
- Have publications at top-tier peer-reviewed conferences or journals
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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Amazon 소개

Amazon
PublicAmazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.
10,001+
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Seattle
본사 위치
$1.5T
기업 가치
리뷰
10개 리뷰
3.4
10개 리뷰
워라밸
2.5
보상
4.2
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3.0
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3.8
경영진
2.7
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Great benefits and competitive pay
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High pressure and long hours
Poor work-life balance
Toxic work culture and management issues
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