Director - AI Strategy & Enablement

dunnhumby
London
2 months ago
Applications closed

Related Jobs

View all jobs

Director of Artificial Intelligence - Manufacturing & Industrial

Director of Data Science & AI – Global Manufacturing Transformation

Director of Healthcare Analytics

Director of Generative AI | Remote

Director of Product - City of London

Director of Data Science & Analytics

dunnhumbyis the global leader in Customer Data Science, empowering businesses everywhere to compete and thrive in the modern data-driven economy. We always put the Customer First.


Our mission:to enable businesses to grow and reimagine themselves by becoming advocates and champions for their Customers. With deep heritage and expertise in retail - one of the world's most competitive markets, with a deluge of multi-dimensional data - dunnhumby today enables businesses all over the world, across industries, to be Customer First.


dunnhumbyemploys nearly 2,500 experts in offices throughout Europe, Asia, Africa, and the Americas working for transformative, iconic brands such as Tesco, Coca-Cola, Meijer, Procter & Gamble and Metro.


We're looking for aDirector of AI Strategy & Enablementwho will be at the forefront of innovation and shape how dunnhumby continues to advance in the field of AI and Data Science. Join us to revolutionise customer experience, drive operational efficiency, and explore new opportunities to elevate our proposition to global retailers and beyond.


Key Responsibilities:
AI Strategy Development:

  1. Develop and refine dunnhumby's AI strategy in alignment with business objectives and industry trends.
  2. Work closely with senior leadership to communicate the AI strategy and gain buy-in across dunnhumby.

Innovation Leadership:

  1. Foster a culture of innovation by promoting creative thinking and experimentation.
  2. Lead the exploration of cutting-edge AI technologies and methodologies to stay ahead of the curve.
  3. Collaborate with cross-functional teams to develop and prototype innovative AI solutions.
  4. Create a pipeline of innovation projects linked to corporate strategy, market dynamics and industry trends.

Partnership Development:

  1. Identify and establish strategic partnerships with industry leaders, research institutions, startups, and other organisations to drive AI innovation.
  2. Lead the academic partnership programme of activity including PhDs.
  3. Leverage partnerships to access resources, expertise, and technologies that complement our AI initiatives.

Cross-Functional Collaboration:

  1. Collaborate with stakeholders across various departments, including corporate strategy, engineering, product management, propositions and marketing to develop and launch new AI-driven offerings.
  2. Provide guidance and support to cross-functional teams to ensure alignment with AI strategy and objectives.

Thought Leadership:

  1. Represent dunnhumby as a thought leader in the field of AI through industry conferences, and publications (articles/ blogs).
  2. Stay abreast of emerging trends, best practices, and regulatory developments in AI and share insights with internal stakeholders.

What we expect from you:

  1. 12-15 years of Corporate experience and background in Data Science and AI.
  2. It is desirable to have a PhD especially in the domains of statistics, science or machine learning.
  3. Commercially sound and a strategic thinker.
  4. Background in Innovation around technology and strategy building.

What you can expect from us:

We won't just meet your expectations. We'll defy them. So you'll enjoy the comprehensive rewards package you'd expect from a leading technology company. But also, a degree of personal flexibility you might not expect. Plus, thoughtful perks, like flexible working hours and your birthday off.


You'll also benefit from an investment in cutting-edge technology that reflects our global ambition. But with a nimble, small-business feel that gives you the freedom to play, experiment and learn.


And we don't just talk about diversity and inclusion. We live it every day - with thriving networks including dh Gender Equality Network, dh Proud, dh Family, dh One and dh Thrive as the living proof. We want everyone to have the opportunity to shine and perform at your best throughout our recruitment process. Please let us know how we can make this process work best for you. For an informal and confidential chat please contact to discuss how we can meet your needs.


Our approach to Flexible Working:

At dunnhumby, we value and respect difference and are committed to building an inclusive culture by creating an environment where you can balance a successful career with your commitments and interests outside of work.


We believe that you will do your best at work if you have a work/life balance. Some roles lend themselves to flexible options more than others, so if this is important to you please raise this with your recruiter, as we are open to discussing agile working opportunities during the hiring process.


For further information about how we collect and use your personal information please see our Privacy Notice which can be found(here).

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.