Senior Data Scientist

Alvarium Talent
City of London
8 months 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)

A specialist data consultancy is seeking aSenior Data Scientistwith strong hands-on experience in Machine Learning and Generative AI. This role offers the opportunity to work on high-impact projects across various industries, applying advanced data techniques to solve real-world business problems.


This is an excellent opportunity to join a collaborative team delivering innovative AI and data science solutions for a diverse client base.


Role Responsibilities

  • Design and implement ML and Generative AI models to address client challenges
  • Work closely with data engineers and MLOps professionals to deliver production-ready systems
  • Apply techniques such as large language models (LLMs), retrieval-augmented generation (RAG), vector databases, prompt engineering, and model fine-tuning
  • Engage with client stakeholders to understand requirements and define appropriate technical solutions
  • Contribute to project scoping, delivery, and reporting, ensuring outcomes align with client objectives
  • Keep up to date with developments in AI and data science and bring fresh ideas to internal and external projects
  • Support a collaborative team culture through knowledge sharing and technical discussions


Skills and experience required

  • Proven experience delivering ML and/or Generative AI projects from concept to deployment
  • Hands-on experience with frameworks such as Hugging Face, LangChain, and open-source LLMs
  • Familiarity with tools such as Databricks and modern MLOps workflows
  • Strong Python skills and familiarity with common data science tools and libraries
  • Experience with cloud platforms (preferably Azure, but AWS or GCP also valuable)
  • Confident communicating complex technical ideas to both technical and non-technical stakeholders
  • Proactive and curious, with a strong interest in emerging AI technologies
  • Experience working in a consultancy or client-facing environment (highly desirable)


If you're passionate about data and AI, and ready to take a leading role in a UK-based consultancy at the cutting edge of the industry, we’d love to hear from you.


Applicants must have the right to work in the UK and not require sponsorship now or in the future.Visa sponsorship is not available.

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.