דרושים Applied Scientist Retail Science ב-אמזון ב-יפן

GAIIC provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and in the cloud.Key job responsibilities
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.Work/Life Balance

- Japanese fluency, Business English
- PhD degree in computer science, engineering, mathematics, operations research, or in a highly quantitative field plus 5 years of relevant experience, or Master’s degree plus 10 years of relevant work experience
- 5+ years of hands on experience with Python to build, train, and evaluate models
- 5+ years of experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high- performance computing
- 2+ years demonstrated experience with Large Language Model (LLM) and Foundational Model post-training, continual pre-training, fine-tuning, or reinforcement learning techniques.
- Scientific publication track record at top-tier AI/ML/NLP conferences or journals
- Experience directly managing scientists or machine learning engineers

You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion.
Key job responsibilities
- Responsible for day-to-day assistance with capacity management.- Deliver simple, sustainable and repeatable solutions and processes.- Manage work and priorities through ticketing system and workflows.- Troubleshooting cabling infrastructure connectivity issues, including patch panels and patch cords.- Participate in the migration, basic configuration and rollout of new or upgraded hardware.- Specifying Power and Cooling requirements and ensuring Hardware Racking/Stacking completed for new equipment.Physical requirements:
- Working in stationary position, with appropriate breaks while maintaining a high level of alertness and attention to detail.- Manual handling and lifting of equipment may be required (weight limits in accordance with regulations)About the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.Work/Life Balance

Education & Certification: Bachelor’s degree in Computer Science or a relevant certification, combined with hands-on experience in networking deployment and cabling infrastructure best practices.Technical Expertise: Strong understanding of enterprise infrastructure technologies including routers, switches, load balancers, and firewalls.Cabling & Tools Knowledge: Familiarity with network cabling standards, optic types, and testing tools such as VFL, power meter, and OTDR.Language Skills: Very good command of English, both written and spoken.

Project & Problem-Solving Skills: Comfortable working on small to medium, and slightly complex projects in ambiguous environments; strong prioritization, time management, and analytical judgment under pressure.Networking Knowledge: Solid understanding of Ethernet, IP networking, and fabric-based network design; experience in large-scale data center network implementations and virtualized enterprise environments.Technical Expertise: Familiar with network cabling, optic types, and test equipment (VFL, power meter, OTDR); strong troubleshooting and support background.Best Practices & Methodologies: Awareness or knowledge of IT best practice frameworks such as ITIL, LEAN, AGILE, and Operational Excellence principles.

In this position, you will be expected to research, design and build/train/tune models and provide recommendations in areas including but not limited to natural language processing (automatic translation, summarization, extraction) and image processing (boundary detection, image understanding, image generation).
Key job responsibilities
As an Sr. Applied Scientist, your responsibilities will be:- Work closely with other Books scientists and engineers to build, review and improve your model design proposals.
- Keep up to date with scientific development in related field

- PhD, or Master's degree and 5+ years of building machine learning models for business application experience
- Knowledge of programming languages such as C/C++, Python, Java or Perl

- PhD in computer vision, natural language processing or equivalent quantitative field
- PhD in engineering, technology, computer science, machine learning
- Business level Japanese language skills

急成長する組織で、データサイエンス・アルゴリズムのスキルを武器に物流に変革を起こしませんか？
アマゾンジャパン トランスポーテーション部門では、Applied Scientistとして、物流ネットワークの最適化・モニタリングに必要なAlgorithm/Heuristicモデル・ソリューションの構築を主導するSenior Applied Scientistを募集します。
Key job responsibilities
• 複雑でBusiness Impactの大きな課題に対するモデル・ソリューションを提供します。アーキテクチャレベルから設計し、必要に応じて他のエンジニアリングチームと協力して行います。
• Networkチーム専属かつ最初のScientistとして、前例のない取り組みに果敢に挑み、ステークホルダーと効果的に連携する必要があります。Tech・Biz両方のManagerとともにロードマップを確立し、ビジネスニーズを満たすソリューションを提供します。ドメインに対する学習意欲と、フルスタックサイエンティスト・エンジニアとして手を動かすことが必要になります。
• ネットワークオペレーション上の基準(正確性、効率性など)や運用上の基準(品質、一貫性、信頼性など)を確立するためのメカニズムをManagerとともに構築します。ユーザーであるネットワークチームに、分析結果を専門用語に頼ることなく効率的に伝えていく必要があります
• 戦略的に考え、トレードオフを行う必要があります。あなたの決定は、Amazonの物流ネットワークに大きな影響を与えます
• アイデアを効果的に伝える必要があり、あらゆるタイプのステークホルダーに対して口頭および文章で伝えることが出来ます。戦略的な文書から、作成したツールの使い方等といった運用上重要な文章まで、あらゆるスタックの文章を作成します。
• 限られたガイダンスの下で、プロジェクトをリードする必要があります。
As the dedicated and first Scientist for the Network team, you will boldly take on unprecedented initiatives and effectively engage with stakeholders. Work with both tech and business managers to establish a roadmap and deliver solutions that meet business needs. A strong learning appetite for the domain and ability to be a full-stack scientist/engineer are required.
Collaborate with managers to establish mechanisms that set network operation metrics (accuracy, efficiency, etc.) and operational standards (quality, consistency, reliability, etc.). You will need to efficiently communicate analysis results to the user network team without relying on specialized terminology.Ability to effectively communicate ideas, both verbally and in writing, to all types of stakeholders. You will produce content ranging from strategic documents to operational guides on using your developed tools across the stack.
Lead projects with limited guidance.A day in the life
• 10:00 出勤。自分がLeadしているプロジェクトのコードを書く
• 10:30 Data Engineerのの同僚が実装した新しい最適化ツールの説明を受ける。前から欲しかったBeam Search Bandwith Scheduler機能がついており感動
• 11:00 ネットワーク最適化シミュレーションのコードを設計。Notebookで実行し、午前中の仕事は終了
• 12:00 ランチ。今週の週替わりメニューのタンドリーチキンを初めて食べてみる。エキゾチック！
• 13:30 Scrum Daily Meeting。同僚の進捗を確認しつつ情報交換。シミュレーションの改良に別プロジェクトの知見が使えそう
• 14:00 Biz sideとフラッシュディスカッション。実装アイデアについて目線合わせ
• 14:30 肩が凝ったので会社でマッサージの施術を受ける。最高
• 15:00 一旦退社。残りは家で
• 16:00 シミュレーションの結果を確認。最適化エンジンはBayesでよさそうだけど、探索空間は要検討。いったん大域探索のために分散並列処理を書く
• 18:00 明日のProject Weeklyの準備。いくつかfigureを作成し、自分の進捗をNotebookに保存
• 18:30 コードレビュー。コメントをいくつか付けてApprove
• 19:30 お仕事終了About the team
弊チームはAlgorithm / Data Scienceソリューションを提供しており、主なミッションは以下の3つです。
• 1: 幹線輸送情報を包括的に管理・分析するための、大規模なData Martの改修・維持管理
• 2: 上記1のデータを利用した最適化・機械学習系アルゴリズムソリューションの提供
• 3: 上記1のデータを利用したBusiness Insight導出用のData Pipeline・Dashboardソリューションの提供
Overhauling and maintaining a large-scale Data Mart to comprehensively manage and analyze long-haul transportation data.Delivering optimization and machine learning-based algorithmic solutions utilizing the data from the above Data Mart.The machine learning-based solutions we have started providing just over a year ago have become a rapidly growing and highly anticipated area.

- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language

Key job responsibilities
As a Science Manager within JP AMZL LMEA team, you will lead a team of data and research scientists towards designing and deploying solutions that will likely draw from a large range of scientific areas such as supervised, semi-supervised unsupervised learning, reinforcement learning, advanced statistical modeling, optimization models and graph models. You will have an opportunity to be on the forefront of supply chain by working on some of the most difficult problems in the industry, with some of the best technical program managers, research scientists, data scientists, engineers, and economists to execute on JP AMZL Science vision and prepare scientific work for production systems integration. You will bring deep technical expertise in the area of Machine Learning and optimization. Other responsibilities include:* Lead a team of data and research scientists towards design, development and evaluation of highly innovative ML/optimization models for solving complex business problems.
* Technically lead and mentor the scientists on the team.
* Research and apply the latest ML techniques and best practices from both academia and industry.
* Use analytical techniques to create scalable solutions for business problems.
* Work closely with BI and data engineers to build relevant pipelines for your models at large scale.
* Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.A day in the life

- 3+ years of scientists or machine learning engineers management experience
- Knowledge of ML, NLP, Information Retrieval and Analytics
- PhD
- Knowledge of machine learning approaches and algorithms
- Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations

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Revolutionize logistics with your data science and algorithm skills in a rapidly growing organization.
Key job responsibilities
• 複雑でBusiness Impactの大きな課題に対するモデル・ソリューションを提供します。アーキテクチャレベルから設計し、必要に応じて他のエンジニアリングチームと協力して行います。
• People Managementを除くTech Leading・Project Leading・Team Leadingを行います。Scrumプロセスに基づき、チームの優先順位を設定し、顧客やステークホルダーと効果的に連携する必要があります。Managerとともにロードマップを確立し、ビジネスニーズを満たすソリューションを提供します。
• エンジニアリング上の基準(正確性、効率性など)や運用上の基準(品質、一貫性、信頼性など)を確立するためのメカニズムをManagerとともに構築します。
• チームの進捗状況、データ/ソリューションの品質、エンジニアリング/運用の状況を測定する指標をManagerとともに定義します。
• 戦略的に考え、トレードオフを行う必要があります。あなたの決定は、組織のインフラストラクチャ(リソースやコストを含む)に影響を与えます。
• アイデアを効果的に伝える必要があり、あらゆるタイプのステークホルダーに対して口頭および文章で伝えることが出来ます。戦略的な文書を作成します。
• 限られたガイダンスの下で、プロジェクトをリードする必要があります。
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• Provide model and technology solutions for complex, high-impact business challenges. Design these solutions from an architectural level, collaborating with other engineering teams as needed.
• Serve as a Tech Lead, Project Lead and Team Lead (excluding people management). Based on Scrum processes, you will set team priorities, effectively liaise with customers and stakeholders. Partner with Managers to establish a roadmap and deliver solutions that meet business needs.
• Collaborate with Managers to establish mechanisms that uphold engineering standards (e.g., accuracy, efficiency) and operational standards (e.g., quality, consistency, reliability).
• Define metrics with Managers to measure team progress, data/solution quality, and the state of engineering/operations.
• Think strategically and make tradeoffs, as your decisions will impact the organization's infrastructure (including resources and costs).
• Communicate ideas effectively, both verbally and in writing, to all types of stakeholders. Produce strategic documentation.
• Lead projects with limited guidance.A day in the life
• 10:00 出勤。自分がLeadしているプロジェクトのコードを書く
• 10:30 同僚が実装した新しいMLOpsツールの説明を受ける。前から欲しかったInput Feature Validation機能がついており感動
• 11:00 プロジェクトメンバーと一緒にHyper Parameter Tuningのコードを設計。Notebookで実行し、午前中の仕事は終了
• 12:00 ランチ。今週の週替わりメニューのタンドリーチキンを初めて食べてみる。エキゾチック！
• 13:30 Scrum Daily Meeting。同僚の進捗を確認しつつ情報交換。Lossの改良に別プロジェクトの知見が使えそう
• 14:00 Biz sideとフラッシュディスカッション。実装アイデアについて目線合わせ
• 14:30 肩が凝ったので会社でマッサージの施術を受ける。最高
• 15:00 一旦退社。残りは家で
• 16:00 Hyper Parameter tuneの結果を確認。Bayesでよさそうだけど、探索空間は要検討。いったん大域探索のために分散並列処理をDEと一緒に書く
• 18:00 明日のProject Weeklyの準備。各メンバーの進捗をSlackで確認し、事前にアドバイスをいくつか提供。Notebookに作ってくれたFigureを保存
• 18:30 コードレビュー。コメントをいくつか付けてApprove
• 19:30 お仕事終了About the team
弊チームはData Scienceソリューションを提供しており、主なミッションは以下の3つです。
• 1: 幹線輸送情報を包括的に管理・分析するための、大規模なData Martの改修・維持管理
• 2: 上記1のデータを利用した機械学習・最適化系アルゴリズムソリューションの提供
• 3: 上記1のデータを利用したBusiness Insight導出用のData Pipeline・Dashboardソリューションの提供----------------• 1: Overhaul and maintain a large-scale Data Mart to comprehensively manage and analyze trunk transportation information
• 2: Provide machine learning, optimization algorithm solutions utilizing the data from the above 1
• 3: Provide Data Pipeline and Dashboard solutions to derive Business Insights using the data from the above 1

- Bachelor's degree
- 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 4+ years of data scientist experience
- Practical knowledge of machine learning model and solution development, as well as the ability to implement them (e.g., data preprocessing, base model selection, architecture design, hyperparameter tuning, MLOps).
- Business Level Japanese or English

- Experience as a leader and mentor on a data science team
- Experience managing data pipelines
- 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience
- Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
- Experience of Optimization such as genetic algorithm, BEAM search, gradient method, Bayesian
- Experience of Graph algorithm such as DFS, BFS, Dijkstra, Maximum Flow, Route Planning, Delivery Planning, Dynamic Programming
- Business Level English

Key job responsibilities
The DE is accountable for:
(1) Defining together with PMs, DEs, and BIEs the requirements and design for a scalable deployment of solutions through paid services, leveraging tools on AWS and CDK.
(2) Work with scientists to build and maintain automated data pipelines for their models.

- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines