Product Manager – Recommendations & AI

About the role

• Full Salary Range - £59,500 - £93,200

• Contract type - This contract is a 12 month contract / secondment.

• Working pattern/flexible working - The Partnership has adopted a hybrid working approach, meaning you'll be able to work a mixture between the office and home based upon your personal needs whilst balancing the needs of the business. The team aims for 1day per week in the office to connect.

• Location - This is a hybrid working role, where your time will primarily be split between working from home (in the UK) and the London Head Office.

As the Product Manager for Recommendations & AI, you will be at the heart of the John Lewis digital experience. Your mission is to harness the power of machine learning and artificial intelligence to create a seamless, deeply personalised shopping journey that feels uniquely "John Lewis." You will lead the strategy for how we use recommendations and AI to surface the right product, to the right customer, at exactly the right moment—minimising friction and maximising discovery.

• Strategic Vision:
  Own the roadmap for our Product Recommendations and AI‑driven features, from personalised product "carousels" and "complete the look" engines to predictive customer intent models.
• Value Maximisation:
  Define and track success through AI‑specific metrics (e.g., CTR, conversion uplift, AOV, and model precision/recall) alongside traditional retail KPIs.
• Backlog & Prioritisation:
  Manage a transparent Product Backlog that balances long‑term algorithmic improvements (e.g., model retraining, data quality) with immediate customer‑facing feature launches.
• Agile Experimentation:
  Champion a "test and learn" culture. You will design and oversee robust A/B and multivariate testing frameworks to validate the impact of your recommendations.
• The Voice of the Customer:
  Deeply understand the "John Lewis Customer" to ensure AI interventions feel helpful and premium, rather than intrusive or generic.
• Technical Partnership:
  Act as the vital bridge between business stakeholders and a highly specialised team of Data Scientists, Machine Learning Engineers, and Data Engineers. You will ensure all AI products are developed with a focus on data privacy, fairness, and transparency, maintaining the trust that is central to the John Lewis brand.

• AI/ML Leadership
  
  Experience:
  
  You bring a proven track record of leading multidisciplinary teams of Data Scientists and Machine Learning Engineers. You understand the ML lifecycle—from data discovery and model training to deployment, monitoring, and scaling.
• Expert Experimentation Capability:
  You are a champion of hypothesis‑driven development. You have deep experience designing and executing sophisticated A/B and multivariate tests, with a firm grasp of statistical significance and the ability to interpret complex data to drive product iterations.
• The Technical "Translator":
  You have a unique ability to bridge the gap between complex algorithmic constraints and commercial outcomes. You can communicate effectively across a wide range of technical and non-technical stakeholders.
• Data‑Obsessed & Analytical:
  You don’t just look at high‑level KPIs; you are comfortable digging into the data to identify bias, understand model performance, and uncover hidden opportunities for personalisation.
• Collaborative Partner:
  You excel at developing rapport with both creative Partners and technical experts, fostering a culture of mutual respect and shared goals within the unique John Lewis partnership structure.
• Future‑Focused:
  You stay ahead of the curve on AI trends (such as Generative AI or Large Language Models) and can pragmatically evaluate how these technologies can be applied to enhance the John Lewis customer experience.

• Experience leading others within a Product team using an Agile development methodology (such as SCRUM or Kanban).
• Experience building complex AI/ML products to solve customer and business problems.
• Proven ability to respond to and prioritise changing demands effectively.
• Ability to balance multiple priorities, stakeholders and timelines.
• Making data‑oriented decisions.