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。

清除筛选

搜索结果: 14,087

← 返回搜索结果

Data Analyst

Emetlab

远程工作

地点
Remote (Worldwide)
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 the Job The Opportunity As a Data Analyst at Emetlab, you will turn data into decisions that shape the infrastructure behind elite AI training data. Emetlab is an AI data infrastructure company delivering expert-produced, rigorously validated datasets and end-to-end AI solutions to frontier laboratories, Fortune 500 enterprises, ambitious startups, and sovereign AI programmes across the globe. In this role, you will uncover insights that drive operations, quality, and business strategy. You will analyse contributor performance, dataset quality metrics, production pipeline efficiency, and client delivery outcomes to help the team understand what is working, what is not, and where the greatest opportunities lie. Your work will directly inform how we maintain our 98%+ accuracy benchmark, how we scale our global contributor network, and how we deliver measurable results to clients. This is an opportunity to be the analytical backbone of a company whose entire value proposition is data excellence, where your insights will carry weight, influence direction, and ensure the quality standard that our clients depend on. What You'll Do Analyse contributor performance, production throughput, and quality assurance metrics to surface actionable insights that inform operations and growth decisions. Design and maintain dashboards, reports, and data visualisations that give leadership and cross-functional teams clarity on key metrics across datasets, contributors, and client delivery. Partner with Data Operations, Engineering, and Product teams to define success metrics, measure outcomes, and evaluate the impact of process improvements and platform changes. Conduct cohort analysis, segmentation, and trend analysis across our contributor network to identify opportunities for improving output quality, efficiency, and retention. Monitor and report on dataset quality benchmarks, identifying patterns in annotation accuracy, inter-annotator agreement, and domain-specific performance. Identify data quality issues, propose improvements to tracking and instrumentation, and ensure the integrity of the operational data we rely on. Translate complex analytical findings into clear, compelling narratives that non-technical stakeholders can act on with confidence. What You'll Need to Succeed 2+ years of experience in data analytics, business intelligence, or a related role within a technology or data-intensive organisation. Strong proficiency in SQL and experience working with analytics tools such as Python, R, Excel, or similar. Experience with data visualisation tools such as Tableau, Looker, Power BI, or Metabase. A strong analytical mindset, comfortable working with large datasets, identifying patterns, and drawing meaningful conclusions. Experience thriving in fast-paced environments where you define the metrics and build the analytical infrastructure that the business relies on. Exceptional communication skills, with the ability to present data-driven insights clearly to both technical and non-technical audiences. A collaborative disposition, comfortable working cross-functionally with Data Operations, Engineering, Product, and Quality Assurance teams. Genuine interest in AI, data quality, or machine learning infrastructure is highly valued. Role Details Employment Type: Full-Time and Part-Time positions available Location: Fully Remote Stage: Growth stage, operational About Emetlab Emetlab is an AI data infrastructure company built on a single conviction: the quality ceiling of any AI system is determined by its training data, not its architecture or compute. We produce expert-crafted, rigorously validated training datasets across 12 specialised domains, delivered by a global network of over 5,000 vetted domain specialists spanning 100+ languages and 50+ specialisations. Beyond datasets, we offer end-to-end AI solutions including Fine-Tuning, RLHF & Alignment, Red Teaming, Model Evaluation, Data Annotation, Synthetic Data Generation, and Pre-Training Data Pipelines. We are not a crowd-sourcing platform. We are the data partner that matches the ambition of the teams building the next generation of AI, delivering measurable results: 42% accuracy improvements, 3x faster time-to-production, and 99.4% regulatory precision. Equal Opportunity Emetlab is an equal opportunity employer. We welcome dedicated individuals regardless of gender, race, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, or veteran status. We believe that extraordinary products are built by extraordinary teams, and extraordinary teams are built on inclusion, respect, and the understanding that brilliance has no single origin. If you require accommodation during the application or interview process, please reach out to our team and we will ensure your needs are met with care. Let's build the future of AI training data, together. 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。