Senior Data Scientist - Product Intelligence Analytics
About this role
To address the challenge of making product telemetry consistent and actionable across an expanding ecosystem, Axon seeks a Senior Data Scientist for Product Intelligence Analytics. Operating out of the Seattle office, the incoming professional will serve as a core analytical partner to product managers, transforming raw usage data into reliable insights that shape high-stakes decisions and guide go-to-market strategies.
Working alongside a senior analytics engineer, the selected candidate will establish shared metric frameworks, context modeling standards, and reporting conventions. Beyond traditional product analytics, the role involves defining how AI and agentic systems consume product data, setting up the necessary data structures and guardrails for automated reasoning. The ideal person combines advanced technical fluency with deep business curiosity, acting as an early-warning system who challenges assumptions and focuses on impact rather than generic reporting.
What you will do
- Act as an analytical thought partner to product managers by pressure-testing questions and proactively identifying risks or leading indicators.
- Define measurement strategies, specify event taxonomies, and validate tracking data to build durable models that drive key product decisions.
- Apply statistical methods such as segmentation, experimentation, and forecasting to transform raw telemetry into business-aligned insight.
- Explore and define how AI agents can perform or accelerate analytics workflows through natural-language querying and automated anomaly detection.
- Collaborate with analytics engineers to establish and evolve shared metric definitions, semantic layer conventions, and reporting standards.
- Maintain high data quality standards and thorough documentation to reduce tribal knowledge and ensure definitions remain discoverable.
What you need
- Bring six or more years of experience in product analytics or a closely related discipline, with a strong focus on user engagement and product adoption.
- Possess advanced SQL and data modeling capabilities alongside working knowledge of programming for analytical workflows.
- Demonstrate hands-on experience with user engagement metrics including activation, retention, stickiness, and feature adoption.
- Show proficiency in applying statistical techniques like experimentation design, causal inference, and forecasting to match business stakes.
- Have familiarity with enterprise B2B go-to-market motions and how product metrics connect to sales, customer success, and renewal cycles.
- Exhibit a proven ability to collaborate with product managers as a strategic thought partner rather than an order-taker.
Nice to have
- Experience working within a native cloud data stack such as AWS-based tooling
- Familiarity with public safety, social impact, or mission-driven product development
Benefits
- Competitive salary and 401k with employer match
- Discretionary paid time off
- Paid parental leave for all
- Medical, Dental, Vision plans
- Fitness Programs
- Emotional & Mental Wellness support
Skills and tech stack
Questions candidates ask
What is the remote work policy for this role?
This role follows a hybrid schedule based out of the Seattle office. Team members work onsite Tuesdays through Fridays, with the flexibility to work remotely on Mondays unless an approved workplace accommodation is in place.
What is the compensation range for the position?
The base pay range is $123,750 to $198,000 USD. Compensation is made up of base pay, bonus, and stock awards, with the actual base pay dependent on level, function, training, transferable skills, work experience, business needs, and geographic market.
Who does this position report to?
This individual contributor role reports directly to the Principal Product Manager of Analytics and has no direct reports.
What kind of collaboration is expected with other engineering teams?
You will partner closely with a Senior Analytics Engineer to shape shared analytics capabilities, including metric frameworks, context modeling standards, and reporting conventions that keep product data consistent across teams.