データサイエンティスト/東京23区の転職求人一覧

We are looking for a Middle/Senior Level Data Engineer to design, build, and operate end-to-end data solutions for a retail client in Japan. Key Responsibilities:Lead the design, build, and optimization of ETL/ELT pipelines to ingest data into the enterprise platform. Drive automation to eliminate manual intervention and enforce rigorous data quality standards. Ensure technical solutions align with enterprise architecture while leveraging new Snowflake and AWS capabilities. Implement metadata management, data lineage tracking, and role-based access controls.#LI-Hybrid#LI-KN

【具体的な業務】・顧客環境（クラウド／オンプレ）における生成AIシステム設計／実装・RAG、エージェント設計、ワークフロー自動化の構築・プロンプト設計、評価設計、品質改善サイクルの確立・API／バックエンド／フロントエンドを含むフルスタック開発#LI-Hybrid#LI-KN

・機械学習アルゴリズム、推論機構、およびマルチモーダル技術の研究を行っていただきます。・また、Research Engineerと密に連携し、研究成果をプロダクトとして最短でプロトタイプへと導いていただくことを期待します。#LI-Hybrid#LI-KN

・機械学習アルゴリズム、推論機構、およびマルチモーダル技術の研究を行っていただきます。・また、Research Engineerと密に連携し、研究成果をプロダクトとして最短でプロトタイプへと導いていただくことを期待します。#LI-Hybrid#LI-KN

・Drafting Advanced Technical Strategy and Service Implementation of Cutting-Edge Technology:Leading the planning and execution of advanced technical strategies, leveraging deep practical technical understanding. This includes selecting technologies for AI (LLMs, etc.), applications, and databases, overseeing design to translate business requirements into high-level engineering specifications, and driving service implementation.・Building and Continuously Evolving Next-Generation Digital Platforms:Responsible for the construction and operation of the next-generation standard digital platform, including core business systems, no-code solutions, data lakes, BI, service buses, and AI Agents. This also involves the continuous improvement of Service Level Agreements (SLA) across the entire group and the enhancement and advancement of the platform.・Managing Hybrid Teams and Optimizing Project QDC (Quality, Delivery, Cost):Forming and managing hybrid

- Own specific technical areas, drive and execute ML product projects, track progress and mitigate risks by collaborating closely with product managers, UX designers, architects, engineers, and stakeholders from other cross-functional teams.- Play a key role in defining system architecture for the ML products and implementing specific components to enhance the user experience.- Design and implement performant, scalable ELT (Extract, Load, Transform) data pipelines, considering an ML model's lifecycle (training and inference).- Take responsibility for technical problem solving and meeting ML product objectives creatively in ambiguous scenarios.