Lead Data Science

Northrop Grumman Careers
Cheltenham
1 year ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist - Marketing Science

Data Science Lead / Manager

Lead Data Scientist

Lead Data Scientist - eCommerce

Lead Data Scientist

Lead Data Scientist


Define Possible at Northrop Grumman UK

At Northrop Grumman UK, our mission is to solve the most complex challenges by shaping the technology and solutions of tomorrow. We call it Defining Possible.

This mind-set goes beyond our customer solutions; it's the foundation for your career development and the impact we have within the community. So, what's your possible?

Salary: £51,100.00 - £76,700.00

Opportunity:

This is more than just a job; it's a mission.

The role is for an experienced data scientist to provide technical delivery and subject matter expertise in data science and machine learning across domains relevant to our customers' missions. This technical role requires a detailed knowledge of a broad range of methods and technologies - with opportunities to work across analytics, data fusion and visualisation, data science, data engineering, machine learning and artificial intelligence - and technical leadership of project teams.

Our UK Cyber & Intelligence business combines modern software development approaches with a rich heritage and experience in the Defence and security sectors. Our customers have complex and sensitive data and information requirements requiring a mission partner who quickly understands the context, delivering and sustaining a portfolio of challenging technology projects at scale and pace, supporting them through an ambitious digital transformation programme.

Responsibilities:

Provide...

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.