Head of Data Analytics / AI

M Group
Stevenage
3 months ago
Applications closed

Related Jobs

View all jobs

Head of Data Analytics

Lead Data/Head of Data Engineer

Head of Data Engineering (AI)

Head of Data Engineering (AI)

Head of Data Engineering (AI)

Head of Data Engineering (AI)


About The Role
Right across infrastructure, theres a requirement to not only maintain, but also renew and reimagine. Whatever stage youre at in your career, with us youll have an opportunity to grow and develop. Delivering essential infrastructure services for life, while being safety first, and client and customer centric in a friendly, fun and respectful environment where you are encouraged to thrive.
Where will you be working?
As an organisation, there is little we dont do and plenty to get involved in. Our Group Support roles are vital in making sure we can help over 11,000 people deliver essential infrastructure seamlessly across water, energy, transport and telecom.
Want to come and be a part of it?
We are seeking a visionary and commercially astute Head of Data Analytics and AI to lead the creation and scaling of a new Group-level function. This is a rare opportunity to drive foundational change, establish AI and data as core enablers, and deliver tangible business value across a complex, multi-platform UK based enterprise of £3 billion turnover and over 20,000 employees.
You will be responsible for setting the strategic direction, building the team and operating model and leading the organisation from a low level of AI maturity to a position of industry leadership.
This will require a leader who thrives in ambiguity, is comfortable building from the ground up, and can drive cultural, technical and or...

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 Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.