Head of Data Science

La Fosse Associates
London
1 year ago
Applications closed

Related Jobs

View all jobs

Head of Data Science

Data Science Manager

Data science programme lead

Principal Data Scientist and Machine Learning Researcher

Vice President, Head of Discovery Data Science

Head of Machine Learning (Recommendations, AI Stylist & Search)

Head of Data Science London (3 days a week in the office)110,000 – 120,000 per annum + share optionsAn exciting opportunity has arisen with a fast-growing fintech business based in London. We’re looking for a hands-on Data Science Leader to take charge of technical data science initiatives, with a strong focus on Credit Risk and machine learning model development. This role is ideal for someone who enjoys solving complex problems and driving technical excellence over traditional management responsibilities.The RoleLead and develop the Data Science team, focusing on technical delivery.Build and deploy advanced machine learning models to support key decision-making processes.Design and implement robust Credit Risk models to drive business growth.Work closely with cross-functional teams to identify opportunities and deliver actionable insights.Stay informed about industry trends and innovations, bringing fresh ideas to the table.ResponsibilitiesProven experience in Credit Risk and machine learning model development.Background in fintech, financial services, or Insurtech preferably.A technical expert who prefers hands-on work over traditional management tasks.Strong track record of delivering impactful data science solutions.Ability to communicate effectively with technical and non-technical stakeholders alike.What’s on OfferA competitive salary of 110,000 – 120,000 per annum, plus share options.Work in a collaborative and innovative environment, with a balance of in-office (3 days a week) and remote working.A chance to shape the future of data science within a growing fintech business.If you have the experience and are looking for a technically focused leadership role in an ambitious and forward-thinking environment, please do apply now!

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.