Data Scientist

Technology and Risk Recruitment LTD
united kingdom
1 week ago
Create job alert

Data Scientist (Remote within the UK)
 
About the Role:
 
We are seeking a highly skilled and innovative Data Scientist with hands-on experience in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), RAG Graph, and Knowledge-Augmented Generation (KAG). You will be working on cutting-edge AI systems that blend open-source technologies and enterprise-scale architecture to solve complex information retrieval and natural language understanding problems.
 
Key Responsibilities:

Design, develop, and deploy RAG pipelines using LLMs (e.g., OpenAI, LLaMA, Mistral, Claude).
Architect and implement RAG Graph systems integrating vector databases, knowledge graphs, and structured data sources.
Lead the design and development of Knowledge-Augmented Generation (KAG) systems for enhanced context-aware reasoning.
Contribute to both open-source initiatives and proprietary enterprise-grade solutions.
Build robust data ingestion, preprocessing, and embedding pipelines for unstructured and semi-structured data.
Evaluate and fine-tune models for performance, relevance, hallucination reduction, and cost-efficiency.
Work collaboratively with ML engineers, data engineers, and software developers to integrate LLM-based components into products.
Stay updated with the latest advancements in AI/LLMs and translate research into applied solutions. Required Skills & Experience:

Strong background in machine learning, NLP, and deep learning.
Hands-on experience with LLM frameworks and APIs (OpenAI, HuggingFace Transformers, LangChain, LlamaIndex).
Experience building RAG pipelines with tools such as FAISS, Weaviate, Qdrant, or Pinecone.
Solid knowledge of graph databases (Neo4j, TigerGraph) and knowledge graph construction.
Proficiency in Python, with deep experience in data science and ML libraries (PyTorch, TensorFlow, Scikit-learn).
Familiarity with MLOps, containerization (Docker), and cloud platforms (AWS, Azure, GCP).
Strong problem-solving skills and the ability to work independently and as part of a collaborative team. Preferred Qualifications:

Experience with KAG systems, semantic search, or context-aware generation techniques.
Contributions to open-source AI projects or community involvement.
Academic background (MSc/PhD) in Computer Science, Data Science, Mathematics, or related fields.
Familiarity with enterprise security, compliance, and data governance considerations for AI applications.  
Why Join Us?

Be part of a dynamic, forward-thinking AI team building next-gen enterprise AI capabilities.
Work with the latest in open-source and commercial AI technologies.
Flexible remote or hybrid work model (UK-based).
Opportunity to shape product direction and AI architecture in real-world enterprise scenarios

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist/Machine Learning Engineer - RNA Design

Data Scientist | London | AI-Powered SaaS Company

Data Scientist - active NPPV3 required

Data Scientist

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Quantum-Enhanced AI in Data Science: Embracing the Next Frontier

Data science has undergone a staggering transformation in the past decade, evolving from a niche academic discipline into a linchpin of modern industry. Across every sector—finance, healthcare, retail, manufacturing—data scientists have become indispensable, leveraging statistical methods and machine learning to turn raw information into actionable insights. Yet as datasets grow ever larger and machine learning models become more computationally expensive, there are genuine questions about how far current methods can be pushed. Enter quantum computing, a nascent but promising technology grounded in the counterintuitive principles of quantum mechanics. Often dismissed just a few years ago as purely experimental, quantum computing is quickly gaining traction as prototypes evolve into cloud-accessible machines. When paired with artificial intelligence—particularly in the realm of data science—the results could be game-changing. From faster model training and complex optimisation to entirely new forms of data analysis, quantum-enhanced AI stands poised to disrupt established practices and create new opportunities. In this article, we will: Explore how data science has reached its current limits in certain areas, and why classical hardware might no longer suffice. Provide an accessible overview of quantum computing concepts and how they differ from classical systems. Examine the potential of quantum-enhanced AI to solve key data science challenges, from data wrangling to advanced machine learning. Highlight real-world applications, emerging job roles, and the skills you need to thrive in this new landscape. Offer actionable steps for data professionals eager to stay ahead of the curve in a rapidly evolving field. Whether you’re a practising data scientist, a student weighing up your future specialisations, or an executive curious about the next technological leap, read on. The quantum era may be closer than you think, and it promises to radically transform the very fabric of data science.

Data Science Jobs at Newly Funded UK Start-ups: Q3 2025 Investment Tracker

Data science has become an indispensable cornerstone of modern business, driving decisions across finance, healthcare, e-commerce, manufacturing, and beyond. As organisations scramble to capitalise on the insights their data can offer, data scientists and machine learning (ML) experts find themselves in ever-higher demand. In the UK, which has cultivated a robust ecosystem of tech innovation and academic excellence, data-driven start-ups continue to blossom—fuelled by venture capital, government grants, and a vibrant talent pool. In this Q3 2025 Investment Tracker, we delve into the newly funded UK start-ups making waves in data science. Beyond celebrating their funding milestones, we’ll explore the job opportunities these investments have created for aspiring and seasoned data scientists alike. Whether you’re interested in advanced analytics, NLP (Natural Language Processing), computer vision, or MLOps, these start-ups might just offer the career leap you’ve been waiting for.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.