Remote Data Scientist (Greece) — LLM & Agentic Systems

Satalia; United States; Remote; USD 51,000 - 73,000; Full time

Job summary

Satalia, fully remote in Greece, seeks a Data Scientist to design and deploy ML/AI models in production, including LLM-powered systems. The role emphasizes end-to-end ownership—from data handling to evaluation—collaborating with engineers and stakeholders across the organisation.

The ideal candidate has 2–5 years of production ML experience, strong modelling skills, and solid software engineering habits. Expect impactful projects and continuous learning in a global, research-driven environment.

Qualifications

• 2–5 years building ML/AI models in production.
• Strong modelling skills for both structured and unstructured data.
• Hands-on experience building LLM-powered systems.
• Good software engineering habits: Python, version control, testing, code review.
• Clear communication with both technical and non-technical stakeholders.
• Experience with temporal and geospatial data.
• Experience building and testing agents.

Responsibilities

• Own well-defined components of a project, from data and modelling to evaluation.
• Develop models, agents, and the evaluation harnesses that keep them safe and reliable.
• Work with engineers to take your work to production.
• Share your work clearly with the team and stakeholders, and support junior colleagues.
• Continently upskill as the field evolves.

Skills

ML/AI models

Production experience

LLM-powered

Python

Version control

Testing

Code review

Communication

Geospatial data

Tools

LLM tooling

Job description

Satalia, fully remote in Greece, seeks a Data Scientist to design and deploy ML/AI models in production, including LLM-powered systems. The role emphasizes end-to-end ownership—from data handling to evaluation—collaborating with engineers and stakeholders across the organisation.

The ideal candidate has 2–5 years of production ML experience, strong modelling skills, and solid software engineering habits. Expect impactful projects and continuous learning in a global, research-driven environment.