

La Fosse
Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Data Scientist focusing on AI & Conversational Systems, offering £800 per day outside IR35 for a hybrid position in London. Key skills include RAG systems, LLMs, Databricks, and SQL expertise.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
-
💰 - Day rate
800
-
🗓️ - Date
April 1, 2026
🕒 - Duration
Unknown
-
🏝️ - Location
Hybrid
-
📄 - Contract
Outside IR35
-
🔒 - Security
Unknown
-
📍 - Location detailed
London Area, United Kingdom
-
🧠 - Skills detailed
#Datasets #Databricks #Scala #AI (Artificial Intelligence) #Databases #SQL (Structured Query Language) #Data Science #Model Evaluation
Role description
Senior Data Scientist – AI & Conversational Systems (RAG / LLMs) - £800 per day Outside IR35
Location: London / Hybrid
About the Role
A leading financial media and data business is building a next-generation AI-powered conversational assistant designed to surface insights across both proprietary datasets and premium editorial content.
The organisation is seeking a Senior Data Scientist to help evolve this platform into a high-quality, production-grade system. The role focuses on improving answer quality, retrieval performance, and real-time responsiveness - translating cutting-edge LLM capabilities into reliable, user-facing products.
Responsibilities
• Develop and optimise RAG (Retrieval-Augmented Generation) pipelines
• Design approaches that combine:
• Unstructured data (articles, reports, documents)
• Structured data (databases, SQL systems)
• Build and refine text-to-SQL capabilities
• Evaluate and improve LLM outputs:
• Accuracy, grounding, and hallucination reduction
• Retrieval relevance and ranking performance
• Design and implement evaluation frameworks (offline and online)
• Collaborate with engineering teams to deploy models into production environments
• Identify and solve real-world implementation challenges
• Contribute to shaping and executing a focused AI roadmap
Requirements
Essential:
• Proven experience delivering production-grade RAG systems
• Deep expertise in working with LLMs for high-quality answer generation
• Databricks experience
• Strong experience with both:
• Unstructured text data
• Structured data / SQL systems
• Experience in model evaluation, experimentation, and optimisation
• Ability to translate complex problems into scalable, practical solutions
Senior Data Scientist – AI & Conversational Systems (RAG / LLMs) - £800 per day Outside IR35
Location: London / Hybrid
About the Role
A leading financial media and data business is building a next-generation AI-powered conversational assistant designed to surface insights across both proprietary datasets and premium editorial content.
The organisation is seeking a Senior Data Scientist to help evolve this platform into a high-quality, production-grade system. The role focuses on improving answer quality, retrieval performance, and real-time responsiveness - translating cutting-edge LLM capabilities into reliable, user-facing products.
Responsibilities
• Develop and optimise RAG (Retrieval-Augmented Generation) pipelines
• Design approaches that combine:
• Unstructured data (articles, reports, documents)
• Structured data (databases, SQL systems)
• Build and refine text-to-SQL capabilities
• Evaluate and improve LLM outputs:
• Accuracy, grounding, and hallucination reduction
• Retrieval relevance and ranking performance
• Design and implement evaluation frameworks (offline and online)
• Collaborate with engineering teams to deploy models into production environments
• Identify and solve real-world implementation challenges
• Contribute to shaping and executing a focused AI roadmap
Requirements
Essential:
• Proven experience delivering production-grade RAG systems
• Deep expertise in working with LLMs for high-quality answer generation
• Databricks experience
• Strong experience with both:
• Unstructured text data
• Structured data / SQL systems
• Experience in model evaluation, experimentation, and optimisation
• Ability to translate complex problems into scalable, practical solutions






