Strategic Overview
AI Data Scientist at Airtm is defined by the responsibilities and candidate requirements in the employer's listing.
The published role emphasizes work such as - Design and deploy AI agent workflows to automate recurring analytical tasks, data summarization, and insight generation pipelines.
The role is listed for LATAM with a remote working arrangement, giving candidates the practical context needed to assess fit before applying.
Key information at a glance
About This Role
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.
Key Responsibilities & Objectives
- - Design and deploy AI agent workflows to automate recurring analytical tasks, data summarization, and insight generation pipelines.
- - 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.
- - Partner with stakeholders to design, develop, and iterate on key metrics, interactive dashboards, and data analyses to support evolving business needs.
Required Qualifications & Candidate Blueprint
- -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.
- - Strong SQL and Python skills for data analysis and modeling.
- - Experience with dbt for analytics engineering workflows.
- - Ability to work cross-functionally and communicate insights effectively.
Preferred Qualifications
- - Exposure to cloud platforms (AWS) for data storage or analytics workloads.
- - Knowledge of feature engineering and model evaluation concepts.
- - Experience with version control (Git).
Salary & Benefits
The available source data does not publish a numerical salary range for this opening. We do not estimate or substitute third-party figures.
Full Job Description
The following is the complete job description supplied by the source. We sanitize unsafe markup and remove external links, but do not shorten or rewrite the employer's source content.
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).
Compensation
Additional Information
Editor’s Market Perspective (Our Custom Analysis)
Why This Position Matters
This opening may suit candidates who can demonstrate experience relevant to the employer's published requirements. Its strongest career signal is the scope of the work itself — particularly - Design and deploy AI agent workflows to automate recurring analytical tasks, data summarization, and insight generation pipelines. — rather than a generic title alone.
Resume & Application Strategy
For the application, connect specific examples from your experience to the responsibilities and qualifications in the employer's description. Quantify outcomes from comparable work, and place the most relevant evidence near the top of the resume. When the employer explicitly names tools, platforms, leadership scope, or domain experience, use those source terms accurately and in context.
We organize source-provided information into this independent job-seeker overview. Compensation is shown only when the source provides it; the official listing remains the authority for current requirements and application instructions.