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| 部署・役職名 | AI Data Scientist |
|---|---|
| 職種 | |
| 業種 | |
| 勤務地 | |
| 仕事内容 |
Responsibilities: - Develop and implement advanced machine learning models and analytical solutions—including Prediction AI, Generative AI, and traditional data science approaches—to address complex business challenges. Leverage a wide range of algorithms, techniques, and platforms (such as OpenAI services and other industry-standard tools) to build, deploy, and scale AI-driven solutions that deliver actionable insights and tangible business value. - Collaborate with Data engineers and IT Subject Matter Experts (SMEs) to understand source data and utilize large datasets to prepare and optimize data for building Generative AI solutions using Open AI services to enhance our products and services and scaling it globally. - Work closely with partners to gather requirements, design data models, and implement tailored solutions. - Standardize processes, techniques, and tools into best practices for other data scientists. - Drive the adoption of data and AI driven solutions by influencing and partnering with key stakeholders. - Innovate and explore creative methods to source and utilize structured and unstructured data for building data and AI driven solutions. Stay updated on industry trends and advancements in AI/ML, with a specific focus on leveraging OpenAI services to continuously enhance our products and services and scaling efforts. - Strong ability to generate relevant, actionable insights based on iterative data analysis, translate data-driven output into business language, and to make appropriate recommendations to respective business stakeholders Independently provides analysis on datasets of significant complexity. - Quickly able to develop enough business insight to connect data sources across multiple diverse and complicated systems. And recommends implementable solutions to (or in collaboration with) business partners. Considers change management in all aspects of their work. - Has fundamental understanding of the business as a whole, and has started to build a network of people for ideation, info sharing, knowledge. - Proactively document and publish innovative initiatives and ideas developed through your work, by submitting papers to academic conferences or presenting at external events and industry forums. |
| 応募資格 |
【必須(MUST)】 Requirements:- An advanced degree in Data Science, Computer Science, Mathematics, Engineering, Statistics, or another quantitative field. - Minimum of 5 years of experience utilizing data science to solve high-impact business problems, with a specific recent focus on building Generative AI applications. f(Insurance preferred but open to financial industry and tech related) - Proficiency in statistical analysis, machine learning, and data manipulation techniques. - Strong programming skills in languages such as Python, R, or SQL, with experience in prompt engineering and relevant packages like LangChain, LangGraph, AutoGen, Llamaindex and more. - Practical experience with big data processing frameworks and platforms such as Azure, Databricks, Spark, with a focus on leveraging these technologies for building and scaling AI solutions. - Experience not only in modeling, but also in integrating AI solutions into production environments, operating them, and continuously improving their performance. - Excellent communication skills – able to explain sophisticated data science concepts to both technical and non-technical business stakeholders and partners, and to collaborate effectively with regional teams and other colleagues across Asia. - Japanese speaking: N2 or N1 level, as long as you can communicate fluently. - Having some references to R&D in your work, academic background, or personal projects is preferable. |
| 受動喫煙対策 | 屋内禁煙 |
| 更新日 | 2025/10/24 |
| 求人番号 | 5960511 |
採用企業情報
- マニュライフ生命保険株式会社
-
- 資本金64,500百万円
- 会社規模501-5000人
- 生命保険
-
会社概要
【設立】1999年3月
【代表執行役社長兼チーフ・エグゼクティブ・オフィサー (CEO)】ライアン・シャーランド
【資本金】645億円(2024年3月31日現在)
【従業員数】2,641名(2024年3月31日現在)
【本社所在地】東京都新宿区西新宿3-20-2
【その他事業所】全国主要都市に営業拠点あり
【事業内容】
生命保険事業
転職・求人情報の詳細をご覧になる場合は会員登録(無料)が必要です