Senior Data Science Engineer (Machine Learning)

dunnhumby is the global leader in Customer Data Science, partnering with the world’s most ambitious retailers and brands to put the customer at the heart of every decision. We combine deep insight, advanced technology, and close collaboration to help our clients grow, innovate, and deliver measurable value for their customers.

dunnhumby employs nearly 2,500 experts in offices throughout Europe, Asia, Africa, and the Americas working for transformative, iconic brands such as Tesco, Coca-Cola, Nestlé, Unilever and Metro.

We’re looking for a Senior Data Science Engineer (Machine Learning) with strong experience building and deploying scalable, production‑ready ML systems. You’ll work with data scientists, engineers, and product teams to develop models that power dunnhumby’s personalisation, recommendations, forecasting, and customer insight products, across areas such as basket understanding, sequence modelling, NLP, multimodal applications, and generative AI.

As part of the AI Strategy, Research & Enablement team, you’ll help shape dunnhumby’s AI direction — turning new research into practical capabilities that influence products, platforms, and long‑term strategy. You’ll work across data science, engineering, and product, partnering with senior leaders to guide priorities and drive innovation.

What we expect from you

• Strong hands‑on experience with modern deep learning frameworks (preferably PyTorch)

• Deep expertise in transformer architectures such as BERT, GPT, T5, and Time‑Series Transformers

• Experience delivering end‑to‑end ML pipelines, including testing, data processing, training, validation, versioning, deployment, and monitoring

• Solid understanding of MLOps tooling (e.g., MLflow, Kubeflow, Airflow, Docker, Kubernetes)

• Strong Python engineering skills, writing clean, modular, production‑ready code (plus bash and Git/GitLab)

• Experience working in cloud environments, ideally GCP or Azure

• Excellent communication skills, able to explain complex concepts to both technical and non‑technical audiences

• Collaborative approach when working with data scientists, product teams, and senior stakeholders

• Experience with large‑scale recommendation systems or other retail‑focused ML applications

• Knowledge of distributed training (e.g., DeepSpeed, PyTorch Distributed)

• Familiarity with vector databases, embeddings, and retrieval techniques

• Understanding of feature stores, metadata stores, and model registries

• Experience working with large datasets, including efficient loading, batching, and streaming (PySpark preferred)

• A practical, delivery‑focused mindset that balances innovation with scalability and robustness

• Curiosity and enthusiasm for applying cutting‑edge ML research to commercial problems

• A drive to raise engineering standards and help evolve dunnhumby’s ML ecosystem

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, dh Enabled 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.

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.

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