AI Data Scientist

Jobtailor; Deutschland; Vor Ort; EUR 70.000 - 110.000; Vollzeit

Zusammenfassung

Airtm is seeking a data scientist to support product and business decisions through analytics, experimentation, and applied data science. You will design AI-powered agentic workflows to automate analysis, surface insights, and scale operations.

Build analytics foundations with SQL and dbt, create Tableau dashboards, run A/B tests, and communicate findings to stakeholders. Collaboration with product and business teams is essential for evolving metrics and dashboards.

Qualifikationen

• 2 to 5 years of experience in similar roles.
• Hands-on experience with AI agent frameworks (e.g., LangChain, LlamaIndex, CrewAI, or similar) and ability to build and deploy agentic systems in 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.

Aufgaben

• 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 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.

Kenntnisse

SQL

Python

Tableau

Statistics

Experimentation

Agent frameworks

Prompt engineering

LLM evaluation

Tools

dbt

LangChain

LlamaIndex

CrewAI

Git

Jobbeschreibung

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.
• 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.
• 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).