Senior Product Manager (Search)
Location: London (2-days per week)
We're partnering with a high-growth, PE-backed technology business on an exciting search for a Senior Product Manager (Search).
Personalisation, relevancy, and search are their biggest growth areas heading into the financial year. This is a senior, technically deep product role focused on building the intelligent data infrastructure that powers personalised experiences. The PM will own search, relevance, ranking models, recommendation systems, and AI-powered personalisation end-to-end from data instrumentation through to model deployment, monitoring, and iteration. This is an AI-native role, requiring genuine hands-on experience with LLMs, semantic search, and ML systems.
The team is made up of product professionals from some of the most respected names in UK tech.
The Role....
* Own the end-to-end product strategy and roadmap for data products, search, relevance, and AI-powered personalisation — translating complex data science and ML capabilities into member-facing features that are intuitive, trustworthy, and measurably impactful
* Define requirements for AI-powered ranking, recommendation, and search systems — including input/output specifications, confidence thresholds, fallback behaviours, evaluation criteria, and post-launch monitoring
* Own the experimentation strategy for the product area — designing A/B tests and multi-armed bandit experiments that generate genuine learning about AI feature performance, not just validate existing assumptions
* Drive the feedback loop strategy — defining how member behaviour is captured, modelled, and fed back into AI ranking systems to create a continuously improving, self-reinforcing product
Requirements...
* Proven direct ownership of AI-powered data products, search, recommendation systems, or personalisation platforms with accountability for the full lifecycle from data instrumentation through to model deployment, monitoring, and iteration
* Specific hands-on experience with search and ranking systems, including semantic search, relevance signals, ranking model evaluation, query understanding, and the product challenges of low-confidence and zero-result states
* LLM product experience with a clear, critical view of where large language models create real leverage — such as query expansion, intent classification, or semantic search, and where you can introduce hallucination risk, latency, or cost constraints that make simpler approaches more appropriate
* Strong experimentation fluency to design and interpret A/B tests, multi-armed bandits, and holdout group experiments in an AI personalisation context, using results to change direction rather than confirm instinct
* Technically credible AI and ML fluency — able to read a model card, challenge a feature engineering decision, write testable acceptance criteria for ML features, and explain confidence intervals to non-technical stakeholders, without overstepping into engineering decisions