About the Job
At Axon, we’re on a mission to Protect Life. We’re explorers, pursuing society’s most critical safety and justice issues with our ecosystem of devices and cloud software. Like our products, we work better together. We connect with candor and care, seeking out diverse perspectives from our customers, communities and ...
Life at Axon is fast-paced, challenging and meaningful. Here, you’ll take ownership and drive real change. Constantly grow as you work hard for a mission that matters at a company where you matter.
This is a Senior Product Analytics role focused on understanding how our products are used across Axon's ecosystem. You will partner closely with a Senior Analytics Engineer to shape shared analytics capabilities — metric frameworks, context modeling standards, and reporting conventions — that keep product data cons...
Key Responsibilities
Location: This role follows a hybrid schedule and is based out of our Seattle office. We rely on in-person collaboration and ask that team members work onsite Tuesdays through Fridays, with the flexibility to work remotely on Mondays, unless there is an approved workplace accommodation.
Reports to: Principal Product Manager, Analytics
Direct Reports: None – this is an individual contributor role
Serve as the analytical thought partner to product managers — pressure-testing whether we're asking the right question before scoping any analysis, and proactively surfacing risks or leading indicators stakeholders haven't asked about. This role is expected to function as an early-warning system, not only respond to requests.
Partner with product managers to define what needs to be measured, specify event taxonomy and tracking requirements, and validate the data lands correctly — turning that foundation into the models and analysis that drive high-quality, high-velocity decisions.
Build and maintain durable data models and applied analyses — using statistical methods (segmentation, forecasting, experimentation) where they add rigor — that transform raw telemetry into business-aligned insight, favoring clear, purpose-built analysis over generic dashboards or one-off exploratory work.
Explore and apply how AI agents can perform or accelerate analytics work — from natural-language querying to automated anomaly detection and insight generation. Define the data structures and documentation that make product analytics legible and reliable for agents, not just humans.
Work alongside a senior analytics engineer to define and evolve shared metric definitions, semantic layer conventions, and reporting standards. Contribute to the conventions that keep metrics consistent and prevent drift as the organization grows.
Maintain data quality standards, testing practices, and documentation norms for your area of ownership. Reduce reliance on tribal knowledge by keeping analysis and definitions discoverable and well-documented.
Required Skills & Abilities
6+ years in product analytics or a closely related role, with strong exposure to user engagement and product adoption measurement, ideally in fast-paced, high-growth SaaS or enterprise B2B environments.
Advanced SQL and data modeling skills, plus working knowledge of programming for analytical workflows.
Direct experience with user engagement metrics (activation, retention, stickiness, feature adoption) and genuine interest in how products earn ongoing use.
Experience defining metric frameworks and shared definitions that hold up across teams.
Comfortable applying statistical techniques — experimentation design, causal inference, forecasting — matching rigor to the stakes rather than defaulting to the most advanced method; includes hands-on experience supporting experimentation frameworks (A/B testing, feature flags).
Familiarity with enterprise B2B go-to-market motions — how adoption, expansion, and engagement metrics connect to sales, customer success, and renewal cycles.
Demonstrated ability to partner with product managers as a thought partner (not an order-taker), including navigating ownership with stakeholders when something looks under-addressed — usually without direct authority over their roadmap.
Genuine curiosity and hands-on exploration of AI/LLM agents for analytics — natural-language querying, automated reporting, anomaly detection, agentic workflows — with a point of view on what data/context agents need to be effective, and thoughtful views on their current limits.
Experience in a native cloud data stack (e.g., AWS-based tooling) — not required, but a plus.
Passion for public safety, social impact, or building products that make a difference.
