Head of Data Science

Harnham
Greater London
8 months ago
Applications closed

Related Jobs

View all jobs

Head of Data Science

Head of Data Science

Head of Data Science

Head of Data Science

Head of Data Science - Advanced Analytics & AI

Head of Analytics & Data Science

Head of Data Science
London – Hybrid
Up to £115,000 + Benefits

The Opportunity
A leading organisation within the Insurance space is seeking a

Head of Data Science

to lead a team of data scientists and drive high-impact machine learning initiatives. This is a unique opportunity to shape the data science strategy, work closely with senior stakeholders, and lead the adoption AI.

Key Responsibilities
Lead and mentor a team of data scientists, from mid-level to lead.
Identify, prioritise, and deliver high-value ML projects aligned with business goals.
Work closely with senior stakeholders to gain buy-in for data-driven strategies.
Oversee model deployment, monitoring, and recalibration to maximise impact.
Ensure full adoption of a Google Cloud-based ML Ops platform.
Drive education and advocacy for data science across the business.

What We’re Looking For
Strong experience leading a high-performing data science team.
6+ Years experience
A track record of developing and deploying commercially impactful ML solutions.
Proficiency in Python, SQL, and cloud-based ML environments (GCP preferred).
Excellent stakeholder management skills—able to bridge technical and business teams.
A proactive problem-solver with a passion for AI and innovation.
Experience deploying large language models in a business setting.
Background in personal lines insurance (health or life).
Experience with Google Cloud ML Ops tooling.

If this looks of interest, please reach out to Joseph Gregory

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.