Lead Data Scientist

Role Overview

IC tech leadership: design, build, test, evaluate, deploy, and monitor machine learning and AI solutions in production, ensuring they are robust, scalable, and aligned to business needs. Includes retrieval, ranking, recommender systems and LLM-based components powering conversational experiences.

Responsibilities

• Develop the team: mentoring and coaching 5 data scientists, creating a high-performing team culture with strong technical standards.
• Collaborate with cross-functional partners (Product, Engineering, Design, Delivery) and online business stakeholders to identify opportunities and deliver impactful customer experiences.
• Build reusable capabilities: identify opportunities to abstract common logic, modules, and functionality across multiple use cases, and convert them into reusable core components, frameworks, and assets.
• Provide technical direction and standards: champion best practices in exploratory analysis, experimentation, model development, evaluation, coding standards, testing, documentation, monitoring, and productionisation.
• Lead a team owning ML/AI problems end-to-end, including LLM-based solutions — from framing and modelling through to production deployment, monitoring, and continuous improvement — delivering measurable business impact.
• Define and instil in the team strong software engineering principles to build robust, scalable ML systems, taking responsibility for performance and reliability in production rather than handing off.
• Apply strong systems and architectural experience — identifying repeatable patterns across use cases and abstracting them into reusable components or shared capabilities that improve the scalability and effectiveness of core algorithms.
• Work with large-scale datasets and distributed data processing (e.g. Spark), and with modern recommendation or personalisation systems at scale (e.g. large-scale retrieval and ranking architectures, real-time inference, vector representations, approximate nearest neighbour search).
• Communicate complex technical concepts, recommendations, and trade-offs clearly and effectively to both technical and non-technical audiences, including senior stakeholders.

Qualifications & Expectations

• Proven track record as a tech lead of a team owning ML/AI problems end-to-end, including LLM-based solutions.
• Strong software engineering principles and the ability to build robust, scalable ML systems.
• Experience in identifying repeatable patterns, abstracting into reusable components or shared capabilities.
• Experience with large-scale datasets, distributed data processing (e.g. Spark), and recommendation or personalisation systems at scale.
• Strong communication skills, able to explain complex technical topics to diverse audiences.

Inclusion & EEO Statement

Marks & Spencer strives to be an inclusive organisation, trusted and admired by colleagues, customers and suppliers. We are committed to building diverse and representative teams, where everyone can bring their whole selves to work and be at their best.

We support each other and work together to win together. If you feel you’d benefit from any support or reasonable adjustments during any stage of the recruitment process, please don’t hesitate to let us know when completing your application.

Benefits

• After completing your probationary period, 20% colleague discount across all M&S products and many third-party brands for you and a member of your household.
• Competitive holiday entitlement with the potential to buy extra holiday days!
• Discretionary bonus schemes awarded based on performance.
• A generous Defined Contribution Pension Scheme and Life Assurance.
• A dedicated welcome with a tailored induction and a range of training programmes.
• Perks and discounts via the M&S Choices portal.
• Industry-leading parental, adoption and neonatal policies.
• Wellbeing support, including 24/7 Virtual GP and PAM Assist.
• A charity volunteer day to support a charity or cause you are passionate about.

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