Lead Data Scientist

We are looking for a Lead Data Scientist to join the eCommerce Data team with a competitive salary range starting from £66,000 alongside great benefits, such as company profit related bonus, private medical insurance, staff discount and more! Based from Next Head Office in Enderby, Leicestershire.

The Role:

eCommerce Data drives the department and the business profitability and growth by powering live trade, merchandising and personalised experiences.

As a Lead Data Scientist at NEXT, you will take ownership of data science solutions end to end - framing the problem, designing the solution and building it into production in front of millions of customers across a catalogue of millions of products and 70+ markets. This is a hands-on, build it yourself role: you write the production code. You will work with state of the art machine learning, including deep learning and modern LLM and agent based systems and prove real impact through rigorous evaluation and experimentation. As a lead, you set the technical direction on your projects, are accountable for the delivery, how it performs and mentor other data scientists. 

What You’ll Take On:

• You will partner with the business, working closely with product, engineering, trading and merchandising teams to understand challenges and opportunities in eCommerce.


• Proactively lead the design, development and deployment of ML/AI solutions - owning the architecture, the trade offs and the delivery plan, writing the design documents the team builds from and mentoring juniors.


• Build machine learning solutions across search, recommendations and personalisation, including training and fine tuning deep learning models where they genuinely beat the simpler option: sequence transformers, two tower retrieval, neural and gradient boosted LTR, multimodal image and text embeddings etc.


• Build LLM and agentic applications - retrieval grounded, tool using, constrained - with the evaluation harnesses that decide whether they are good enough to ship and keep them honest once they have.


• Deliver and maintain containerised training and inference, orchestrated ML pipelines, feature and model registries, model serving, drift monitoring, retraining and rollback.


• Design and run experiments that settle the question - clear hypotheses, agreed guardrails and honest readouts, including the ones that say the model did not win.


• Set the standards for data quality, model governance, evaluation and documentation, and raise the bar through design and code review.

What You’ll Bring:

• Significant experience in applied data science or machine learning with a degree in computer science, mathematics, or related field.


• A track record of owning solutions end to end and shipping models that moved a business metric.


• Strong expertise in advanced statistics, machine learning, deep learning and hands-on experience with training and fine-tuning large models.


• Excellent Python, SQL, PySpark, Pytorch/Tensorflow and git experience with production grade software engineering discipline - code standards, version control, testing, code review, and documentation.


• Practical experience building and evaluating LLM and agentic systems — RAG, tool use, orchestration frameworks such as LangGraph, and offline and online evals for non-deterministic systems.


• Experienced designing and running A/B tests and other forms of experimental design. Causal inference, off policy evaluation and bandits are nice to have.


• Strong communication skills with the ability to translate complex data insights into actionable business strategies, and to explain a modelling trade-off to a non-technical audience.


• Experience/exposure to Docker, K8s, CI/CD and react/next.js, Triton, vLLM etc is a plus.

You do not need to tick every single requirement but a drive to learn and develop is important.