Senior Data Scientist - AI

Harnham
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist (GenAI)

Senior Data Scientist

Senior Data Scientist & Machine Learning Researcher

Senior Data Scientist (GenAI)

Senior Data Scientist (GenAI)

Senior Data & AI Scientist | Up to £80,000 + 10% Bonus | Hybrid (London 3 days)


Are you passionate about shaping the next generation of AI-powered products?


Join a dynamic, fast-moving business that’s embedding AI into everything from customer interactions to internal tools.


We’re looking for an experienced Data & AI Scientist to help drive innovation across cutting-edge AI initiatives — from chatbots and voice assistants to advanced retrieval-augmented generation (RAG) systems and agentic workflows.


The Role

You’ll work closely with the AI Engineering and Data Science teams to:

  • Develop and prototype AI-driven solutions across customer-facing and internal applications.
  • Build and optimise LLM-based assistants, RAG pipelines, and agentic AI workflows.
  • Collaborate on the architecture and deployment of scalable AI solutions (with support from engineering).
  • Partner with stakeholders to translate business needs into practical, intelligent systems.
  • Mentor junior team members and contribute to the evolution of our AI tooling stack.


This role is ideal for a hands-on AI practitioner who enjoys experimentation, collaboration, and delivering real-world value.


What You’ll Bring

  • 3+ years experience in a commercially driven customer environment
  • Strong Python skills and solid grounding in AI/ML fundamentals.
  • Experience with some of Databricks, LangChain, vector databases, or LLM orchestration tools.
  • Knowledge of RAG pipelines, transformers, and modern AI architectures.
  • Understanding of deployment or MLOps best practices (Azure experience a plus).
  • Recent, hands-on experience with applied LLM projects.
  • Strong communication and stakeholder engagement skills.


A Master’s degree in Computer Science, ML, or AI is preferred but not required.


The Opportunity

  • Be part of an ambitious AI journey - already 70% built, with big plans for the next 5 years.
  • Contribute to short-term LLM “quick wins” and longer-term agentic AI development.
  • Work in a collaborative, supportive environment with senior data leaders.
  • Help shape how AI transforms both customer experience and business efficiency.


Package & Details

  • Salary: Up to £80,000 + 10% Bonus + 5% Matched Pension
  • Hybrid working: 3 days per week in London office (Tues/Weds fixed)
  • No visa sponsorship available


Apply below!

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

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

Industry Insights

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

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.