Senior Product Manager - Intelligence

About the role

What you'll do

• Own the Intelligence roadmap, OKRs and delivery, with two or three big bets each quarter. You make the prioritisation calls, own the business outcome behind the backlog, and write the release notes when things ship.
• Set the evaluation bar for AI across Osome: eval datasets, confidence thresholds, human-review sampling and go/no-go gates.
• With our Security team, set the safety and data-handling bar for AI across Osome: escalation rules, pre-action checks on agents, fail-open vs fail-closed behaviour, privacy and retention, client data isolation, and provider governance (e.g. zero data retention agreements).
• Define the success metrics for each AI product before it ships, then keep checking the data yourself after launch to see whether it's working. Brief the analyst on what to build, and review the result. Use that evidence to make model and provider calls, e.g. benchmark a new model against our own labelled documents before switching.
• Break epics into clear stories and tasks that engineers can pick up without a meeting. Pair with product teams' engineers to build AI features, and define what "successfully handed over" means.
• Be the team's liaison with Operations: run training and feedback rounds, collect baseline data, and drive adoption until the AI version beats the manual process.
• Run a small research track: test new techniques and approaches (e.g. retrieval, memory or agent patterns) on our own problems, and write up what we should adopt and what we should skip.
• Represent this team's priorities to senior stakeholders.
• As the team grows, mentor other PMs and help manage them.

What you'll bring

• Evaluated against our PM capability framework. At the Senior PM level, that means:
• Strategic impact: you can define and drive a strategy spanning 12 months or more for a platform-style team whose customers are other teams, tied to a company-level goal such as gross margin.
• Domain ownership: you own the OKRs, roadmap and business outcome of a complex, ambiguous, cross-functional area. You understand applied LLM/ML products deeply (evaluation, retrieval, memory, agent safety), and can become credible in our accounting and operations domain within two quarters.
• Collaboration: you drive high adoption of new tools with operations teams, including the processes needed to run them. You build consensus among senior leaders with conflicting goals, and you can say no while leaving the stakeholder feeling heard.
• Problem-solving: you map ambiguous, system-level problems, and know when a rule, a classifier or an LLM is the right tool.
• Execution: you reliably ship complex, cross-functional launches involving Operations and other product teams. You prototype to de-risk before committing engineering time, and you spot risks early.
• AI-literacy: hands-on well beyond prompting. You build prototypes and ship small PRs with AI coding tools, build eval datasets and run model experiments, read traces, and judge where models fail.
• Leadership: you shape direction across teams you don't manage, improve how the team works, and are ready to mentor at least one other PM as a player-coach.
• AI/ML features shipped to production, with quality measured after launch (accuracy, coverage, deflection).
• Experience building evaluation datasets and running model experiments: thresholds, go/no-go gates, human-in-the-loop decisions, and reading traces in an observability tool (we use Langfuse) to see where models fail.
• Experience shipping AI features that handle sensitive or financial data, and deciding when they should escalate to a human or stop.
• Daily use of AI coding tools (e.g. Claude Code, Codex, Cursor) to read code and ship small changes.