AI Data Scientist
Airtm helps users move money across borders by providing a digital dollar wallet and peer-to-peer exchange platform.
Funding history
About Airtm
Airtm helps freelancers, entrepreneurs, and businesses move money across borders by providing a digital dollar wallet and peer-to-peer exchange platform. With it, users can receive payments from global platforms, convert to local currency through multiple payment methods, and access US virtual banking services.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will support product and business decisions through analytics, experimentation, and applied data science. You will design AI-powered agent workflows, assess LLM tools, define metrics and KPIs, maintain reliable SQL and dbt analytics foundations, and build Tableau dashboards. You will conduct A/B tests and exploratory analyses, communicate recommendations clearly, and partner with stakeholders to evolve metrics, dashboards, and analyses.
Requirements
- 5+ years of experience in similar roles
- Experience with AI agent frameworks such as LangChain, LlamaIndex, or CrewAI
- Experience building and deploying agentic systems
- Prompt engineering and LLM output evaluation experience
- Experience orchestrating multi-step AI pipelines with LLMs, structured data sources, APIs, or internal tooling
- Strong SQL and Python skills
- Experience with dbt
- Experience building Tableau dashboards
- Knowledge of statistics, experimentation, and hypothesis testing
- Cross-functional communication skills
Responsibilities
- Design and deploy AI agent workflows for recurring analysis, data summarization, and insight generation
- Evaluate and integrate LLM-based tools for analytical use cases
- Collaborate with product and business teams to define analytical questions, success metrics, and KPIs
- Build and maintain analytics foundations using SQL and dbt
- Design, build, and maintain Tableau dashboards
- Perform A/B testing, statistical inference, significance testing, and result interpretation
- Conduct ad-hoc, exploratory, and statistical analyses
- Communicate findings and actionable recommendations to technical and non-technical stakeholders
- Develop and iterate on key metrics, dashboards, and data analyses with stakeholders
