Data Science Lead

McGregor Boyall
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Science Lead

Data Science Lead

Data Science Lead

Data Science Lead / Manager

Data science programme lead

Data science programme lead

Responsibilities

of a GenAI Data Science Lead: Build and lead a high-performing Data Science, Generative AI, AI/ML Engineering & Ops team using agile practices Drive P&L, formulate growth strategies, and achieve revenue targets for the Data Science practice Architect and deliver cutting-edge data science and Generative AI/ML solutions for clients across industries Lead end-to-end sales processes, including solution design, consultative selling, and RFP responses Establish thought leadership and contribute to marketing initiatives, industry events, and conferences Orchestrate large, multi-service line deals and drive strategic client engagements

Requirements for a GenAI Data Science Lead:

10+ years of experience in Data Science, AI/ML transformations with top-tier brands, especially in the UK & Europe Deep expertise in Generative AI, AI/ML practices, tools, techniques, and industry trends Proven leadership in building and managing diverse, high-performing teams Experience with consultative selling, solution design, and delivering data transformation projects Strong business acumen, with a track record of achieving sales targets and developing new business Exceptionalmunication, relationship-building, and stakeholder management skills Passion for innovation, strategic thinking, and a "will-to-win" attitude Master's degree or higher inputer Science, Data Science, or a related field


If you are a dynamic leader with a passion for data science and AI/ML, and a proven track record of driving growth and delivering exceptional customer experiences, click apply!

McGregor Boyall is an equal opportunity employer and do not discriminate on any grounds.

Job ID RK00030

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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.