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'll work closely with Product, Marketing, and cross-functional stakeholders to define metrics, analyze performance, design experiments, and deliver insights that drive impact across the organization.
Key Responsibilities
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Collaborate with product and business teams to define analytical questions, success metrics, and KPIs.
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Build and maintain analytics foundation using SQL and dbt, enabling reliable reporting and self-serve analytics.
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Design, build, and maintain Tableau dashboards that bring metrics to life and support day-to-day decision-making.
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Perform A/B testing and experimentation, including experiment design, statistical inference, significance testing, and result interpretation.
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Perform ad-hoc, exploratory, and statistical analyses to uncover insights and validate hypotheses.
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Communicate findings clearly to both technical and non-technical stakeholders, translating data into actionable recommendations.
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Partner with stakeholders to iterate on metrics, dashboards, and analyses as business needs evolve.
Qualifications
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Strong SQL and Python skills for data analysis and modeling.
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Experience with dbt for analytics engineering workflows.
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Experience building dashboards in Tableau (or similar BI tools).
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Solid foundation in statistics, experimentation, and hypothesis testing.
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Ability to work cross-functionally and communicate insights effectively.
Nice to Have
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Experience building or prototyping machine learning models.
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Exposure to cloud platforms (AWS) for data storage or analytics workloads.
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Experience working with data pipelines or collaborating closely with data engineering teams.
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Knowledge of feature engineering and model evaluation concepts.
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Experience with version control (Git).
