Senior Product Manager

Why you'll love this role:

This is a 12-month contract with genuine scope and real ownership. You won't be inheriting a polished system; you'll be shaping one that has serious room to get smarter.

• You'll work at the intersection of ML, UX, and data, not just on top of it
• You'll have the autonomy to design experiments, make calls, and drive direction without waiting on approvals
• You'll partner with a cross-functional team across engineering, data science, design, content, and GTM that's invested in getting this right
• You'll see the direct impact of your work in user behaviour, model performance, and platform intelligence over time

What you'll own:

You'll lead product across search, recommendations, content intelligence, and agentic discovery. That means shaping how the platform understands content, models user preferences, and surfaces the right learning at the right moment, across standalone, embedded, and API-based experiences.

The systems under your remit:

• High-intent search — improving relevance, ranking logic, and retrieval quality
• Personalized recommendations — evolving ML models and designing feedback loops that compound over time
• Intelligent filtering & categorization — making it easier for users to navigate a growing content library
• Content metadata enrichment — strengthening accuracy and coverage so the system understands what it's working with
• Preference & behavioural modelling — building personalization logic based on role, industry, region, and real usage signals
• Agentic discovery & curation workflows — designing systems that don't just respond to users, but proactively help them move forward
• Experimentation frameworks — building the scaffolding that lets the team test, measure, and iterate quickly
• Explainability — designing AI-driven experiences users can understand and trust

What you'll spend your day doing:

• Breaking down complex, ambiguous problems into small, high-impact releases that ship quickly and generate real signal
• Working closely with data scientists to understand model trade-offs, experiment design, and ranking logic
• Partnering with engineering to evolve traditional ML recommendation models and experiment with modern AI techniques
• Collaborating with design, content, and GTM teams to keep momentum high and decisions clear
• Surfacing trade-offs early and making calls with imperfect information, without getting stuck in analysis paralysis
• Running experiments, reading behavioural data, and iterating based on what you find rather than what you assumed
• Thinking commercially about how discovery quality flows through to activation, engagement, retention, and partner value