Data Scientist

Data Scientist GenAI / Chatbot

Seniority: Medior–Senior
Language: English
Working arrangement: Hybrid, with Thursdays in the office in Utrecht

Your role
As a Data Scientist focused on GenAI and chatbots, you’ll help improve the quality and effectiveness of chatbot systems. You’ll work at the intersection of data, engineering, and product thinking, analysing chatbot behaviour, identifying weaknesses, and turning insights into meaningful improvements.
Your focus will be on understanding why a chatbot isn’t performing as expected and what context or structure it needs. You’ll assess current solutions against state-of-the-art approaches and contribute to improvements in retrieval, generation, reasoning loops, tool calling, and related GenAI capabilities.

Your responsibilities

Analyse chatbot outputs and identify improvements with the greatest impact.
Diagnose context gaps and determine what additional information agents need.
Work with product managers and engineers to define criteria for evaluating agent performance.
Collaborate with use-case teams and stakeholders to understand real-world scenarios and turn feedback into improvements.
Improve retrieval mechanisms, data structuring, and the onboarding of contextual sources.
Design evaluation frameworks for rapid, large-scale experimentation and continuous innovation.
Translate functional requirements into actionable technical improvements.

Your experience and skills

A strong analytical mindset and a passion for understanding AI system behaviour.
A strong understanding of GenAI systems and experience with LLM-based applications.
Experience with retrieval-augmented systems.
A strong understanding of prompts, context windows, retrieval, and grounding.
The ability to evaluate output quality and explain why results are effective or ineffective.
Familiarity with API integrations, batch synchronisation, and structured and unstructured data.
Comfort collaborating closely with engineers and troubleshooting data-flow and context-ingestion issues.
The ability to design evaluation frameworks beyond traditional machine-learning metrics.
An understanding of functional context and user behaviour, and the ability to work across engineering and product domains.
Clear communication skills, including explaining context gaps and reasoning to engineers, product managers, and stakeholders.
The ability to create clear and effective improvement proposals.

Nice to have

Experience with human-in-the-loop evaluation.

Who will thrive in this role
You enjoy understanding why AI systems behave the way they do and finding ways to improve them. You like debugging behaviour as well as code, think structurally, connect the dots, and have a strong intuition for what good performance looks like.

Eisen

• Strong understanding of Gen
• AI systems
• Experience with LLM-based applications
• Experience with retrieval-augmented systems
• Strong understanding of prompts, context windows, retrieval, and grounding
• Ability to evaluate output quality and explain why results are effective or ineffective
• Familiarity with API integrations, batch synchronisation, and structured and unstructured data
• Comfortable collaborating closely with engineers
• Ability to troubleshoot data flow and context ingestion issues
• Ability to design evaluation frameworks beyond traditional machine learning metrics
• Understanding of functional context and user behaviour
• Ability to operate effectively between engineering and product domains
• Ability to explain context gaps and reasoning to engineers, product managers, and stakeholders
• Ability to create clear and effective improvement proposals

Gunningscriteria

• Background in human-in-the-loop evaluation