Product Manager, Analytics

We're looking for a product manager to turn Intelligems' data into decisions merchants can actually act on.

This PM leads our Analysis pillar: analytics surfaces, sitewide reporting, post-test metrics, benchmarking, recommendations, and AI-powered interpretation. Intelligems has a data asset most competitors can't replicate, and this PM exists to make that advantage real for merchants who don't have a data team.

This is a high-impact, highly cross-functional role. You'll work closely with data engineering, data science, and the broader product team to define what merchants see, how they interpret it, and how AI closes the distance between data and confident action. The job requires someone who builds for non-analysts, has genuine opinions about where opinionated product experiences matter and where data infrastructure matters more, and can hold their own in technical conversations about how data gets computed and delivered.

What You'll Do

Make data actionable for non-analysts

• Lead product for analytics and insight surfaces with one clear mandate: showing data and creating understanding are not the same thing. Every surface — from sitewide P&L to post-test metrics — should help a merchant who isn't a data expert reach a faster, more confident decision.
• Drive interpretation, not just display. The gap between a chart and a decision is where merchants struggle.
• Define which views, metrics, and analytical lenses matter most to merchants navigating the platform.
• Own the analytics experience within experiments, from in-test performance through post-test metrics that reveal the full impact story of a test weeks and months after it ends.

Own benchmarking

• Define how merchants understand their performance relative to peers. What does "good" look like for their industry and traffic mix? And how does that drive their next set of optimization actions?
• Work with data science on the methodology and infrastructure behind peer groups and industry norms

Build AI-powered interpretation and recommendations

• Define the product strategy for AI summaries, chat, and "interpret this" experiences across analytics surfaces
• Shape how we structure and expose Intelligems data through our MCP layer so AI agents can query it reliably
• Build toward proactive insight delivery. Merchants should learn something important before they think to find it themselves.

What You Bring

Required Experience & Skills

• Product and design craft: you have a specific point of view on what makes a complex, flexible product accessible to a non-technical user. You've made real simplification decisions and can describe what you gave up to get there. You work with design as a genuine creative partner, not just a handoff
• AI-native product instinct: you think about AI as the mechanism that does the work, not a feature to add. You understand what it means for a product surface to be reliably operable by an agent and have genuine opinions on where AI helps and where it creates friction
• Technical agency: you don't need to code, but you stay in technically hard problems until a path emerges. You've found solutions through constrained environments before, and you think about what serves the merchant and what the team can actually ship in the same breath

Ideal Background

• 3+ years of product management experience, with meaningful time working with analytics — either shipping or exhibiting an affinity for data-first products
• B2B SaaS experience with real customers and real stakes
• Experience working closely with data engineering or data science teams, not just consuming their output, but scoping and prioritizing the work alongside them
• Experience advocating for product quality and launch reliability, not just feature delivery
• Demonstrated ability to make data legible and actionable for non-analysts — the merchants using this product are running e-commerce businesses, not data science teams, and you've built for that reality
• Familiarity with the metrics that move e-commerce businesses: CVR, RPV, AOV, LTV, subscription attach rate, and why they move independently of each other
• Not afraid of sharing ideas by building them — you’ve used AI tools to create working prototypes, test concepts, or demo features
• Bonus: familiarity with Shopify or any product that runs inside a third-party storefront environment
• Bonus: experience building alongside or shipping agentic AI workflows

Key Attributes

• Craft-oriented — specific opinions about what makes an experience good, and holds that bar under shipping pressure
• Technically curious — engages with hard technical problems rather than routing around them; stays in the problem until a path emerges
• User-centered — builds from the non-technical merchant's experience, not the feature spec; knows the difference between functional and trustworthy
• AI-native — thinks about what it means for products to be built by agents, not just assisted by them, and has opinions about what that requires
• Collaborative — works closely with design, engineering, and the broader product team; understands that craft at this level is a team sport