Product Intelligence Manager

Klarity•San Francisco, CA

1d•$179,000 - $230,000•Onsite

About The Position

Every product decision at Klarity should be grounded in evidence. 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. The ideal candidate is an analytics practitioner with product instincts, a measurement architect, AI-aware, a translator between data and decisions, and a self-serve builder. Experience in product analytics, data science, or analytics engineering is required, with direct product accountability preferred. B2B SaaS or AI platform experience is a plus.

Requirements

Analytics practitioner with product instincts
Fluent in SQL
Comfortable building data models
Confident designing instrumentation schemas from scratch
Measurement architect
Build event taxonomies that hold up, and dashboards PMs actually open
AI-aware
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
Translator between data and decisions
Synthesize complex analysis into clear narratives for product and leadership
Self-serve builder
Build tooling and documentation that empowers PMs and designers to answer their own questions
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

Nice To Haves

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.

Responsibilities

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 of view