寻找你的下一个职业机会

按职位、技能和地点搜索招聘信息。准备申请前,先仔细了解职位要求。

找到一个吸引人的职位名称只是求职的起点。请将工作职责、招聘要求和工作条件与你的实际经历进行比较。本指南帮助你筛选机会、准备有针对性的申请材料,并确认每份申请应该在哪里提交。

此界面为简体中文。雇主发布的职位名称和描述保留原文,可能为英文。

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Data Scientist (Remote, GB)

Atos

远程工作

地点
Remote (United Kingdom)
工作安排
Full Time
发布日期
2026年10月15日

申请前,请在雇主网站确认职位仍在招聘,并检查完整要求和条件。

职位描述

此界面为简体中文。雇主发布的职位名称和描述保留原文,可能为英文。

About Atos Group Atos Group is a global leader in digital transformation with 52,000 employees and annual revenue of c. € 10 billion, operating in 54 countries under two brands — Atos for services and Eviden for products. European number one in cybersecurity, cloud and high-performance computing, Atos Group is committed to a secure and decarbonized future and provides tailored AI-powered, end-to-end solutions for all industries. Atos is a SE (Societas Europaea) and listed on Euronext Paris. The purpose of Atos is to help design the future of the information space. Its expertise and services support the development of knowledge, education and research in a multicultural approach and contribute to the development of scientific and technological excellence. Across the world, the Group enables its customers and employees, and members of societies at large to live, work and develop sustainably, in a safe and secure information space. Key Responsibilities Develop scalable APIs and data services that expose trusted and governed data products and implement solutions that support both operational reporting and advanced analytics use cases. Develop web applications and internal data products using modern front-end frameworks such as React, Angular, Vue or similar. Develop and integrate REST APIs and, where appropriate, GraphQL APIs. Implement authentication and authorization mechanisms using enterprise identity providers. Collaborate with UX, Product and Business stakeholders to translate requirements into usable solutions. Test and operationalize predictive models and machine learning solutions. Collaborate with business stakeholders to identify opportunities for optimization, forecasting, automation and intelligent decision support. Support the deployment of machine learning models into production environments using MLOps practices. Work with structured, semi-structured and streaming data to generate actionable insights; Contribute to AI-enabled solutions including Generative AI, Agentic AI, NLP, forecasting and optimization where appropriate; Apply Infrastructure-as-Code principles to support repeatable cloud deployments. Build dashboards and visual analytics solutions using modern reporting and visualization tools; Translate complex analytical outputs into clear, business-focused insights. Work closely with Data Scientists, Analysts and operational stakeholders to maximize adoption of delivered solutions. Develop and maintain automated tests including unit, integration, contract and performance testing as appropriate. Understand and support security-by-design principles including least-privilege access, secrets management, network security and encryption controls. Essential Skills & Experience Software Engineering Good understanding of REST APIs and event-driven architectures. Experience applying software engineering practices to the development of reliable, maintainable applications and data products. Data Engineering Strong practical data engineering experience. Understanding of partitioning, offset management, delivery guarantees and event-driven solution patterns. Experience building data pipelines and real-time or near-real-time processing solutions. Experience working with structured and semi-structured data. Full Stack Development Experience developing user interfaces using a modern front-end framework such as React, Angular or Vue. Experience integrating front-end applications with APIs and data services. Cloud & Platform Engineering Good understanding of CI/CD practices and DevSecOps principles. Experience working with containerized applications and cloud-based environments. Data Science & Analytics Practical experience using Python for data science, automation or analytics. High-level appreciation of machine learning techniques, feature engineering and model deployment. Experience operationalizing analytical solutions within enterprise environments. Ability to collaborate effectively with Data Scientists and other analytical specialists to translate analytical approaches into production solutions. Desirable Skills & Experience: Experience developing production-grade user experiences and data visualizations. Experience with MLOps platforms and model lifecycle management. Familiarity with optimization, forecasting and simulation techniques. Experience building or contributing to AI-enabled operational products. Exposure to Generative AI, Agentic AI or NLP solutions. Knowledge of data governance, metadata management and data quality frameworks. Some grounding in statistics and analytical problem-solving. Experience with Infrastructure-as-Code tools and approaches. Benefits: Pension Scheme - contributions matched up to 10% Private medical cover Income Protection Life Assurance 25 days paid leave + National Holidays Flex benefits program Diversity, Equity & Inclusion At Atos, we want everyone to feel confident and supported throughout the recruitment process. As a proud signatory of the Armed Forces Covenant, Employers Domestic Abuse Covenant (EDAC) and Care Leaver Covenant, we welcome applications from individuals from all backgrounds and life experiences. As a Disability Confident Leader, we guarantee an interview to disabled applicants who meet the minimum essential criteria for the role. If you require any reasonable adjustments during the recruitment process, please let us know and we will ensure the appropriate support is in place. Please contact the recruiter managing this vacancy or reach out to our dedicated mailbox: . We're here to help ensure you can participate fully and showcase your skills and experience. Originally posted on Himalayas

查找职位、比较要求,再准备申请

从你希望从事的职位或运用的技能开始搜索。调整地点和职业筛选,打开职位比较工作职责。如果没有结果,可以使用更短的关键词,或逐一移除筛选条件。

区分必备要求和优先条件,检查已列出的工作安排、薪资和地点。远程职位也可能要求特定居住国家、工作许可或工作时间重合。请向雇主确认职位信息和完整条件。

选择能够回应职位要求的真实经历,说明你的贡献,只使用能够证实的数字。遵循雇主的申请说明,并在提交前检查联系方式、文档内容和 PDF。

提交申请前的检查清单

求职常见问题

为什么有些职位使用英文?

职位名称和描述由招聘企业撰写。为避免改变招聘要求或工作条件,我们保留原文。操作界面和本指南使用简体中文。如果中文搜索没有结果,可以尝试使用职位发布语言中的名称或技能,例如“software engineer”。搜索词不会自动翻译,界面语言也不代表企业要求的申请语言。

远程职位是否允许从任何国家工作?

不一定。企业可能对居住国家、工作许可或工作时段有要求。请查看原始招聘页面中的具体条件。如果未说明,应先向企业确认,再判断能否从你所在的地区工作。“远程”标签本身并不代表没有地点限制。

搜索没有结果时应该怎么办?

尝试更通用的职位名称或单个技能,并逐一移除筛选条件。不同企业可能用不同名称描述相似工作。如果某个职位已经消失,请在企业招聘页面搜索其职位编号。扩大搜索范围不会让已经关闭的职位重新开放。

申请会通过 ResumizeAI 直接提交吗?

申请按钮会打开外部网站。请按照企业或招聘服务的说明,在该网站完成并确认提交。在 ResumizeAI 中准备简历并不等于已经申请职位。如果链接只打开企业网站,请先找到对应职位,再继续申请流程。

如何针对职位调整简历和求职信?

将招聘要求与能够解释清楚的项目、任务和成果联系起来。突出相关经历,不要添加未经实际掌握的技能或虚构成绩。在求职信中用具体例子说明申请动机,并遵守企业要求的语言和文件格式。提交前检查两份文件,确保内容准确、联系方式正确、链接可用。