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# Data Scientist (Gen AI)

- **Company:** Sky UK
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Query Languages, Python (Programming Language), Machine Learning, Chatbots, Pytorch, Large Language Models, Generative AI, Scikit Learn, Xgboost
- **Published:** July 26, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=d9f9c43a68b0ffb9

## About the Role

* Advanced degree (MSc or PhD) with specialization in Statistics, Data Science, Machine Learning, Physics, Mathematics, Operations Research, Engineering or another quantitative field, or equivalent industry experience.

 * Hands-on experience developing solutions with Large Language Models.

 * Hands-on experience building applied ML models using Python and relevant libraries and frameworks (e.g., scikit-learn, XGBoost, PyTorch).

Desirable skills and experience:

 * Experience designing evaluation frameworks for generative systems (e.g., LLM-as-a-judge, task-specific metrics).

 * Experience with vector databases, data querying languages and retrieval methods.

 * Experience with AI monitoring, observability, guardrails, testing and responsible AI practices.

 * A commercial mindset, you understand how data science drives value, not just insight.

## Description

We're on a mission to make Sky smarter, faster, and more customer-centric by harnessing the power of Generative AI. Sitting at the heart of Sky's innovative Group AI function, the GenAI delivery team blends deep technical expertise with commercial focus to deliver next-gen AI solutions that power everything from customer agent knowledge to conversational AI.

You'll work alongside engineers, product, and domain experts to shape the way Sky uses Data Science and Large Language Models to unlock real value across the business.

What you'll do

 * Develop Generative AI and agentic solutions, including RAG applications, conversational assistants, intelligent workflows and tool-using agents.

 * Design and run experiments to test and validate AI/ML approaches, including LLM evaluation frameworks.

 * Analyse in-depth datasets, traces and system performance to continuously improve AI solutions.

 * Collaborate across engineering, product, and commercial teams to take AI-driven solutions from ideation through deployment.

 * Strengthen our internal data science capabilities by continuously driving experimentation, innovation, and continuous learning.


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