Data Scientist - Agentic AI Systems

TheLoops; Deutschland; Remote; EUR 50.000 - 75.000; Vollzeit

Zusammenfassung

A technology-driven company in Germany is seeking a Data Scientist with strong research skills and over 4 years of experience in data science and AI research. The role focuses on designing research initiatives for AI agents, building models, and collaborating with cross-functional teams. Ideal candidates are comfortable with experimentation and have experience in agent frameworks. Join us to translate insights into deployable features that enhance agent performance.

Qualifikationen

• 4+ years of experience in data science, applied ML, or AI research.
• Experience with agent frameworks (AutoGen, OpenAgents, LangGraph, etc.) is a plus.
• Strong research mindset and comfort with experimentation.

Aufgaben

• Design and run applied research initiatives for AI agents.
• Build and evaluate models for agentic systems.
• Develop tools and pipelines for testing agent behavior.
• Analyze data from user-agent interactions and experiments.
• Prototype and test hypotheses for agent performance.
• Collaborate with teams to translate insights into features.
• Communicate findings to technical and non-technical audiences.

Jobbeschreibung

What we're looking for?

We need a Data Scientist with a strong research mindset and 4+ years of experience in data science, applied ML, or AI research. You should be comfortable with fast-paced experimentation and model development, with a track record of translating insights into deployable features. Bonus points if you have experience with agent frameworks (AutoGen, OpenAgents, LangGraph, etc.) or a background in decision-making models, memory systems, or multi-agent coordination.

What you'll do:

• Design and run applied research initiatives that inform the behavior and learning loops of AI agents.


• Build and evaluate models for planning, memory, retrieval, reasoning, or tool use in agentic systems.


• Develop internal tools and pipelines to test agent behavior across different environments.


• Analyze structured and unstructured data from user-agent interactions, logs, and experiments.


• Rapidly prototype and test hypotheses to improve agent performance and reliability.


• Collaborate with engineering, product, and design to translate insights into deployable features.


• Communicate findings clearly and concisely to both technical and non-technical audiences.