Senior Product Analyst

About 9fin

9fin is the AI platform powering global debt markets — the world’s largest asset class at over $145 trillion.

Debt markets are vast, global, and mission-critical, yet still run on fragmented data, PDFs, and manual workflows. 9fin replaces this broken infrastructure with a single platform that centralises proprietary credit data, deep analysis, and high-value workflows across global markets.

Today, 9fin powers teams at 300+ blue-chip institutions worldwide, including global banks, asset managers, private equity firms, law firms, and advisors. The business is scaling at exceptional speed, with rapid expansion in the US and best-in-class retention driven by deep workflow adoption.

We’re at a defining inflection point. With proven product-market fit and strong, global market pull, 9fin is accelerating toward becoming the category-defining platform for debt markets worldwide.

What you’ll work on

You'll sit at the core of how 9fin builds our product; turning behavioural data into the decisions that shape our roadmap. Working alongside another product analyst and our analytics engineers, you'll own product analytics end-to-end, from instrumentation through to the insight that changes what we build.

• Own North Star Metrics across product squads. Define, sign off and implement NSMs for each product team end-to-end, from metric definition with PMs, to dbt models on BigQuery, to Omni topics and dashboards that become the source of truth.
• Build and own product data models. Author and maintain dbt models, tests and docs that turn raw event streams into reliable, reusable product datasets, and own the Omni semantic layer (topics, metrics, dimensions) so stakeholders trust the numbers.
• Own instrumentation as a governed product. Design and maintain the event taxonomy, embed a tracking spec into every PRD before build, and partner with engineering to reconcile front-end (Amplitude) and backend events so data is trustworthy by design.
• Drive post-launch, retention and activation analysis. Diagnose how features land — measuring adoption, retention, time-to-value and activation by persona, using Amplitude, SQL and Omni to turn findings into clear recommendations.
• Be the analytical partner to Product. Embed in discovery and PRDs, define success metrics, size opportunities to inform prioritisation, and translate ambiguous questions into analysis that drives roadmap decisions.
• Answer high-impact ad-hoc questions. Rapidly investigate usage, segmentation, funnels and data-quality issues for product, leadership and commercial stakeholders.
• Connect product usage to commercial outcomes. Move from user-level to account- and seat-level analytics — breadth and depth of adoption, seat/licence utilisation and champion engagement — to surface account-health signals and product-qualified accounts that predict renewal, churn and expansion, in partnership with our GTM analytics.
• Establish a fit-for-scale experimentation and causal-inference approach. Pragmatic methods suited to our sample sizes (Bayesian readouts, holdouts, pre/post and quasi-experiments, guardrail metrics), and the judgement to know when to triangulate rather than A/B test.
• Apply predictive modelling where it drives action. Churn and expansion propensity models that feed commercial and product decisions.
• Raise the bar on AI product analytics. Measure and improve the quality of our AI products (AI Chat, AI Research Grid) through resolution, feedback loops, cost-per-query and AI-specific retention.