AI数据科学家
AI Data Scientist
关于公司：
Airtm 是一家金融基础设施公司，致力于构建在线工作经济的未来。我们致力于赋能全球南方地区日益增长的数字创业者，赋予他们财务自由，以实现繁荣发展。
问题显而易见：在新兴市场，接入美元经济体系非常困难。跨境支付速度慢、成本高，并且经常因通货膨胀而贬值。这限制了数百万有才华的人士的潜力。
Airtm 的解决方案是一个快速且全面的金融平台，能够实现低价值的跨境支付和本地提现。作为稳定币支付基础设施的先驱者，Airtm 已经构建了市场上最先进的跨境支付系统。
作为一家与在线工作世界紧密相连的公司，Airtm 将超越支付领域，打造在线工作经济所需的必要基础设施。我们正在培育一个全新的经济体系，为个人、社区和国家提供掌控自身财务命运的工具。
职位描述：
我们正在寻找一位数据驱动、充满好奇心且善于协作的数据科学家，通过分析、实验和应用数据科学来支持产品和业务决策。在此职位中，您将负责定义新指标、验证数据以生成洞察，并负责我们数据基础设施的端到端生命周期——从创建和维护 Tableau 仪表板到构建和调整数据字段和表。此外，随着人工智能能力重塑数据团队的运作方式，您将在设计和部署 AI 驱动的代理工作流方面发挥积极作用，以大规模自动化这些分析、维护和建模任务。
主要职责
- 设计并部署 AI 代理工作流，以自动化重复性分析任务、数据摘要和洞察生成流程。
- 评估并整合基于大语言模型（LLM）的工具到数据团队的工作流程中，评估其可靠性、准确性和适用于分析用例的适配性。
- 与产品和业务团队合作，定义分析问题、成功指标和关键绩效指标（KPI）。
- 使用 SQL 和 dbt 构建和维护分析基础架构，实现可靠的报告和自助分析。
- 设计、构建并执行 Tableau 仪表板的每周维护，使指标可视化，确保数据准确性及日常决策的平台稳定性。
- 执行 A/B 测试和实验，包括实验设计、结果分析和结论汇报。
查看英文原文
About us:
Airtm is a financial-infrastructure company building the future of the online-work economy. We are on a mission to empower the world's growing number of Digital Entrepreneurs in the Global South, giving them the financial freedom to thrive.
The problem is clear: in emerging markets, accessing the dollar economy is difficult. Cross-border payments are slow, expensive, and often lose value to inflation. This limits the potential of millions of talented individuals.
Airtm’s solution is a swift and comprehensive financial platform that facilitates low-value cross-border payments and local cash-outs. As pioneers in stablecoin-payment infrastructure, Airtm has built the most advanced cross-border payment system available on the market.
As a company married to the world of online work, Airtm will go beyond payments to build the necessary infrastructure the online-work economy needs to thrive. We are fostering an entirely new economy, giving individuals, communities, and countries the tools to take control of their financial destinies.
About the role:
We're looking for a data-driven, curious, and collaborative Data Scientist to support product and business decision-making through analytics, experimentation, and applied data science. In this role, you will be responsible for defining new metrics, validating data to generate insights, and owning the end-to-end lifecycle of our data infrastructure—from creating and maintaining Tableau dashboards to building and adapting data fields and tables. Furthermore, as AI capabilities reshape how data teams operate, you'll play an active role in designing and deploying AI-powered agentic workflows to automate these analysis, maintenance, and modeling tasks at scale.
Key Responsibilities
- Design and deploy AI agent workflows to automate recurring analytical tasks, data summarization, and insight generation pipelines.
- Evaluate and integrate LLM-based tools into the data team's workflow, assessing their reliability, accuracy, and fitness for analytical use cases.
- Collaborate with product and business teams to define analytical questions, success metrics, and KPIs.
- Build and maintain analytics foundation using SQL and dbt, enabling reliable reporting and self-serve analytics.
Design, build, and perform weekly maintenance of Tableau dashboards to bring metrics to life, ensuring data accuracy and platform stability for day-to-day decision-making.
- Perform A/B testing and experimentation, including experiment design, statistical inference, significance testing, and result interpretation.
- Perform ad-hoc, exploratory, and statistical analyses to uncover insights and validate hypotheses.
- Communicate findings clearly to both technical and non-technical stakeholders, translating data into actionable recommendations.
- Partner with stakeholders to design, develop, and iterate on key metrics, interactive dashboards, and data analyses to support evolving business needs.
Qualifications
-5+ Years of experience in Similar roles
-Hands-on experience with AI agent frameworks (e.g., LangChain, LlamaIndex, CrewAI, or similar) and demonstrated ability to build and deploy agentic systems in a production or near-production context.
- Proven experience with prompt engineering and evaluating LLM outputs for data-related tasks such as automated reporting, anomaly narration, or natural language querying.
- Experience orchestrating multi-step AI pipelines that combine LLMs with structured data sources, APIs, or internal tooling.
- Strong SQL and Python skills for data analysis and modeling.
- Experience with dbt for analytics engineering workflows.
- Experience building dashboards in Tableau
- Solid foundation in statistics, experimentation, and hypothesis testing.
- Ability to work cross-functionally and communicate insights effectively.
Nice to Have
- Exposure to cloud platforms (AWS) for data storage or analytics workloads.
- Knowledge of feature engineering and model evaluation concepts.
- Experience with version control (Git).