Location
London

Hours
Full Time

Salary
Competitive, commensurate with experience

About the Role
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. Employing nearly 2,500 experts worldwide, dunnhumby works with transformative, iconic brands such as Tesco, Coca-Cola, Nestlé, Unilever, and Metro.

Joining the AI Strategy, Research & Enablement team, you will help shape the future of AI across the organisation. Working at the intersection of cutting-edge research and real-world commercial application, you will turn innovative ideas into scalable capabilities that influence products, platforms, and long-term strategy. You will collaborate with senior leaders and contribute to decision-making across data science, engineering, and product, helping to prioritise and advance the right AI investments for the future.

As a Senior Research Data Scientist, you will play a key role in developing dunnhumby's next-generation models across a range of strategic AI initiatives. You will focus on advanced modelling, long-term capability building, and cross-industry collaboration. Your responsibilities will include designing new approaches, building and testing prototypes, supporting engineering teams with productionisation, and contributing to partnerships with academia and external research organisations.

Key responsibilities include:
- Driving the design, experimentation, and evaluation of transformer-based models on retail challenges
- Exploring innovative architectures such as sequence-to-sequence models, causal transformers, and hybrid retrieval + transformer approaches to improve personalisation, recommendations, and forecasting
- Developing research-grade prototypes and collaborating with engineers to translate them into scalable, production-ready solutions
- Conducting forward-looking research on generative AI, multimodal learning, and representation learning
- Identifying emerging AI/ML techniques with high potential for retail and consumer analytics
- Driving internal thought leadership by defining standards, sharing knowledge, and advising teams on ML adoption and best practices
- Collaborating with academic groups and private-sector research partners to co-develop new modelling approaches and explore cutting-edge AI applications
- Mentoring other data scientists and contributing to organisational capability in deep learning and advanced ML
- Partnering closely with internal teams to align research pathways with tooling and platform strategy
- Documenting best practices and contributing to reusable assets, libraries, and internal frameworks

Experience
- Expertise in transformer architectures such as BERT, GPT, T5, and Time-Series Transformers
- Strong hands-on experience with modern deep learning frameworks, preferably PyTorch
- Solid grounding in machine learning fundamentals and statistical modelling
- Familiarity with distributed training and GPU acceleration
- Proven track record of delivering impactful models in production or research settings
- Strong data manipulation and engineering skills including PySpark, SQL, and cloud-based data tooling
- Experience working on large datasets with efficient loading, batching, and streaming (PySpark preferred)
- Collaborative experience working with ML engineers, product managers, academic partners, and leadership stakeholders

About You
- Ability to take ambiguous research ideas and shape them into structured investigations or prototypes
- Curiosity and a learning mindset, tracking state-of-the-art advancements and evaluating their relevance
- Strong communication skills, capable of explaining complex ideas to both technical and non-technical audiences
- Passion for turning cutting-edge modelling ideas into practical business impact
- A mindset blending scientific curiosity with real-world pragmatism
- Enthusiasm for shaping the next generation of AI capabilities through hands-on modelling and strategic influence

Qualifications
- Advanced degree in a relevant field (e.g., Computer Science, Data Science, Machine Learning) or equivalent practical experience preferred
- Nice to have: experience in retail, CPG, recommendations, or customer behaviour modelling
- Engagement with academic research including publications, workshops, or industry collaborations
- Familiarity with vector databases, embeddings, and retrieval techniques