Product Intelligence Manager

About Klarity

Tldr on us:
Series B, $91M raised, 7x growth last year.

Digital Transformation gave rise to a $600B consulting Industry. AI Transformation will be 10x bigger and will be delivered through agents. We built that.

Our AI discovers how work actually happens across every team and application, structures it into a living Context Graph, and improves it continuously.

Service Now mapped 900+ processes in 9 days. Door Dash captured 3,800+ finance operations in 14 weeks. That's not a project. That's compounding intelligence.

OpenAI, Google, Door Dash, and Stripe use Klarity to transform how they transform. We shipped GPT-4 document chat within 12 hours of OpenAI's API launch.

We move fast, reward agency, and care deeply about our customers, our team, and our mission.

Every product decision at Klarity should be grounded in evidence. Right now, that's harder than it should be. This role exists to fix that – to build and own the measurement layer from the ground up: instrumentation, eval frameworks, self-serve tooling, and the culture of using data to make decisions rather than to justify them after the fact.

• Analytics practitioner with product instincts — Fluent in SQL, comfortable building data models, confident designing instrumentation schemas from scratch. You think about analytics as a product: it needs adoption, and it needs to solve real problems for the people using it.

• Measurement architect — You design metric frameworks before features launch, not after. You build event taxonomies that hold up, and dashboards PMs actually open.

• AI-aware — You understand what it means to evaluate AI product performance: output quality metrics, user trust signals, and the difficulty of measuring success when outputs are probabilistic. You've thought about this seriously, not in the abstract.

• Translator between data and decisions — You synthesize complex analysis into clear narratives for product and leadership. You don't just surface numbers — you drive action from them.

• Self-serve builder — You build tooling and documentation that empowers PMs and designers to answer their own questions. You're not a bottleneck.

• Experience: 4–7 years in product analytics, data science, or analytics engineering, at least 2 years with direct product accountability. B2B SaaS or AI platform experience preferred.

• Product & UX Taste — You catch gaps in how the product is measured before PMs ask. You design instrumentation that surfaces the right signal at the right time. You tell the difference between metrics that reflect what users actually do and metrics that make the team feel good.

• AI-Native Velocity — You operate at the intersection of AI product development and measurement. You design eval frameworks for AI feature outputs, automate your own analytics workflows, and move fast enough to keep pace with a team shipping weekly.

• Ships to Insight — You own the full instrumentation and analytics stack end-to-end. You don't wait for engineering to build tracking — you get it done. The bar is shipping the infrastructure that makes evidence-based decisions the default, not generating reports after the fact.

• Communication — You translate complex analysis into clear, decision-driving narratives. You create artifacts — tracking plans, data quality docs, eval frameworks — that non-technical stakeholders can actually act on.

• Talk directly with customers and customer executives to understand how they use the product and what they need from it.

• Own Klarity's event taxonomy, instrumentation standards, and analytics tooling — and keep it current as the product evolves.

• Build measurement frameworks before major launches — partner with TPMs and engineers so success is defined before a line of code is written.

• Build the eval infrastructure for AI features: output quality, user trust, adoption, downstream business impact.

• Deliver the analytics the product org needs: funnel analysis, feature adoption, cohort studies, retention.

• Build self-serve capability so PMs and GTM can answer their own questions without waiting on you.

• Bring data-backed recommendations to product planning — not just analysis, but a point…