About the role
Airtm · Remote
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. As AI capabilities reshape how data teams operate, you'll play an active role in designing and deploying AI-powered agentic workflows that automate analysis, surface insights, and augment how the team operates 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.
- 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 maintain Tableau dashboards that bring metrics to life and support 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 iterate on metrics, dashboards, and analyses as business needs evolve.
Qualifications
-2 to 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.
-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 (or similar BI tools).
- 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).
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