AI Industry Solutions Lead

Capgemini
London
9 months ago
Applications closed

Related Jobs

View all jobs

AI Transformation Consultant: GenAI & MLOps at Scale

AI & Data Science Manager / Senior Manager

Machine Learning and AI Engineering Lead

Senior Data Scientist – AI for Engineering & Simulation

Senior Data Scientist

DV-Cleared Data Scientist – On‑Site in Manchester (ML/GenAI)

Job Title:AI Industry Solutions Lead


Get The Future You Want!

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.


Your Role

The AI Industry Solutions Lead role lead will use our AI use case exploration approach combined with their own experience and knowledge to identify, scope and run these use case exploration projects working with stakeholders from across Capgemini and our customers. Specific responsibilities include:

  • Manages and successfully executes assigned AI use case explorations through the entire lifecycle
  • Collaborates with cross-functional partners from product, architecture, BD, Risk, Legal, Data Privacy, AI Governance etc. to achieve successful outcomes
  • Facilitates workshops to upskill key partners and teams on innovation mindset and methods
  • Working with the AIIS Manager, helps iterate on the AI use case exploration process to develop new methods and tools based on sprint experiences and our partner feedback
  • Continuously research and implement brand new AI and machine learning techniques to improve capabilities, ensuring Capgemini remains at the forefront of AI Innovation in the financial industry.


Your Profile

The ideal candidate will have 5-10 years of experience in innovation, AI, project planning in the financial services industry. Key attributes below:

  • A background of working in an agile product environment and/or experience applying design thinking principles in a product development context
  • Strong experience in analysing complex business problems and translating them into structured data science projects and AI powered solutions
  • Ability to operate in a fast-paced, ever-evolving technological landscape
  • Experience of sourcing and prioritising customer needs in a product development lifecycle
  • Excellent collaboration skills, communication skills, stakeholder management skills and ability to inspire and motivate others around shared goals
  • Strong organization and planning skills with the ability to prioritise workload when multiple projects are on the go
  • Entrepreneurial, creative and passionate about solving tough challenges
  • Relevant industry knowledge and experience in financial services, ideally banking, payments or securities
  • Brings a Consulting mindset to work with the customers to understand their challenges and address them using our frameworks and innovation toolkit
  • A can-do attitude and drive to achieve excellence in all the work they do.


About Capgemini

Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world while creating tangible impact for enterprises and society. It is a responsible and diverse group of 350,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market-leading capabilities in AI, cloud, and data, combined with its deep industry expertise and partner ecosystem. The Group reported 2023 global revenues of €22.5 billion.


Get The Future You Want |www.capgemini.com

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.

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

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.