Data Science Lead — AI Platform Quality

Capgemini; Wrocław; Hybrid; PLN 260,000 - 380,000; Full time

Job summary

Capgemini is seeking a Data Science Lead for Neo to define and govern data science practices across the platform. You will ensure high model quality, robust evaluation, and enterprise-grade deployment readiness while collaborating with product, engineering, and knowledge teams.

You will guide experimentation, evaluation frameworks, and prompt assessment, shaping standards for embeddings, vector search, and AI features across Capgemini’s AI initiatives.

Qualifications

• 7+ years of experience in Data Science with strong exposure to NLP, retrieval augmented systems, LLMs, or applied AI solutions.
• Proven experience leading Data Science teams and defining experimentation and evaluation frameworks.
• Strong knowledge of LLMs, embeddings, vector search, and model evaluation techniques.

Responsibilities

• Lead Data Science activities across multiple streams, ensuring consistent methodologies, evaluation standards, and model quality.
• Define and implement experimentation frameworks, test protocols, and evaluation strategies for AI features.
• Establish best practices for prompt evaluation, feasibility assessments, and iterative model improvement.
• Oversee how AI models interact with Neo’s knowledge base to ensure accuracy, relevance, and controlled behaviour.
• Collaborate with engineering and architecture teams to prepare models for production deployment.
• Act as the central authority on AI model quality, validation, and alignment across Neo.
• Drive continuous improvement of Data Science standards as the platform evolves.

Skills

Data Science leadership

NLP

LLMs

Vector search

Python

Cross-functional leadership

Education

Bachelor’s or Master’s in Data Science/CS/Applied Maths

Tools

Python

Notebooks

AI experimentation tooling

Job description

Capgemini is seeking a Data Science Lead for Neo to define and govern data science practices across the platform. You will ensure high model quality, robust evaluation, and enterprise-grade deployment readiness while collaborating with product, engineering, and knowledge teams.

You will guide experimentation, evaluation frameworks, and prompt assessment, shaping standards for embeddings, vector search, and AI features across Capgemini’s AI initiatives.