Director Data Analytics & AI

KPMG
Edinburgh
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Director, Data Science and Analytics

Head of Data Science, Analytics and Reporting

Head of Data Science, Analytics and Reporting

Data Science Graduate

Data Science Graduate

Data Science Graduate

Job description

Director – AI

 

The Team
Many of our clients are undergoing rapid digital transformation, leveraging AI including Generative AI (GenAI) to drive operational efficiencies, enhance customer experiences, and mitigate reputational, financial and regulatory risks. Our highly specialised Data and AI team applies cutting-edge AI/GenAI techniques and industrial-scale cloud platforms to accelerate these journeys. Our projects involve processing vast and complex data sources, developing advanced AI models, and deploying scalable AI-driven solutions that create tangible business value.

 

Our UK-based AI team collaborates globally with KPMG's business, engineering and cloud specialists to design and implement state-of-the-art AI systems and products. We work across diverse industries, including Financial Services, Retail, Public Sector, Healthcare, Energy, and Utilities, enabling AI-powered decision-making and automation.

 

At KPMG, we believe in fostering an inclusive and diverse environment. Our strength lies in our people—their expertise, innovation, and unique perspectives. Join us to be part of an exciting AI-led transformation.


Roles & Responsibilities

 

Technical Leadership:

 

Lead and manage teams developing AI and GenAI solutions for enterprise-scale challenges, including:Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG)Autonomous AI Agents for workflow automationAI-powered decision intelligence systemsAI ethics and responsible AI implementation Deliver AI solutions across the full lifecycle, from PoCs and MVPs to production-grade deployments. Ensure AI model robustness, governance, security and regulatory compliance.

 

Business Development & GTM:

 

Develop market-facing AI offerings to drive revenue growth. Work closely with industry sector teams to tailor AI solutions for specific market needs. Shape AI/GenAI-enabled transformation programs and secure new business opportunities through innovative AI-driven solutions. Drive RFP responses and business development efforts, crafting compelling AI proposals that showcase technical excellence and commercial viability. Contribute to AI thought leadership by authoring white papers, participating in industry panels, and presenting at AI/GenAI conferences.

 

People:

Play a key role in the leadership team, driving the growth and development of our AI capability. Support team expansion through hiring, mentoring, and coaching colleagues to foster a high-performing, inclusive culture. Contribute to knowledge management, ensuring best practices, insights and innovations are effectively shared across the team. Oversee resource planning and process optimisation, enhancing operational efficiency and scalability.

 

The Person

Significant level of experience in AI, data science, data engineering and/or other technology related capabilities in one or multiple industries. BSc (MSc or PhD preferred) in Computer Science, Statistics, Engineering or similar technical field Strong background in AI engineering, software engineering, data engineering and data platforms, with a track record of overseeing full-stack development and delivering production-grade solutions. Proficient with programming languages used by data scientists and AI engineers such as Python, R, Scala. Proficient with modern development tools such as Git and Docker. Up-to-date knowledge and hands-on experience with AI/ML technologies, including a broad range of libraries and tools such as Azure AI/ML Studio, Azure OpenAI, GCP Vertex AI, LangChain, LangGraph, PyTorch, Scikit-learn. Excellent problem-solving skills and the ability to work collaboratively in a cross-functional team. Strong communication skills with the ability to communicate complex technical concepts clearly to non-technical stakeholders. Strong planning and organisation skills to work with a high-performance team, handle demanding clients and multitask effectively.

#LI-EH1

 

 

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.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.