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Law enforcement agencies need training systems informed by real data about how officers perform in the field. We've spent the last few years collecting that data and validating the data products that create actionable insights that trainers can use to make encounters more effective, less injurious, and safer for everyone involved. We've shipped the simple models, but now we need to get our customer-validated models into production. Your job is not just to ship them, but to ensure that we have a strategic pipeline to continue delivering data products for our customers for years to come. What works in research needs to be production-grade, reliable, and reusable.

We're a small, technically deep team embedded in Axon's TASER pillar. Our work spans hardware telemetry and behavioral science - we analyze TASER device data, build models that drive training recommendations for law enforcement agencies, and ship the analytical tools that get those insights to the people who can act on them. Our analyses reach the C-suite. Our models become user-facing features. We're working toward Axon's Moonshot: reduce fatal officer-involved shootings by 50% in the next decade.
The head of product was a Staff Engineer. Our TPM ran a 60-person engineering organization. Both product managers have quantitative degrees - applied mathematics and engineering. Our program manager for TREND is a former state police lieutenant with over 30 years of experience in law enforcement. Our designer is part of the team, not separated from it. We built this team to cover the full space - engineering, data, product, and domain - and this hire is the next deliberate addition.
This is the opposite of a silo - and it comes with a tradeoff: you won't always have another SWE in the room, so you'll need to be technically self-sufficient. What you get in return is a team with more depth of experience per person than most engineering environments you've worked in.

Location: This role is based out of one of our US-based offices (Seattle or Scottsdale) and follows a hybrid schedule. 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: Director, TASER Data Science

• Build and ship data products: dashboards, metrics systems, and recommendation tools that drive real decisions
• Own production ML deployment - bring models from research to reliable production systems with monitoring, versioning, and operational rigor
• Build and own data pipelines from TASER device telemetry through to analytics surfaces used by agencies and internal stakeholders
• Set technical direction for the team's engineering practices - the data scientists here write code and want to do it better; you'll be the senior engineering voice they've been missing
• Work across the full stack - device-side data ingestion through user-facing analytics - and move between projects to build breadth
• Use AI tools as a core part of your development workflow, not a novelty

Must-haves:

• You write production code at a high standard - strongly typed, comprehensively tested, designed for the people who will maintain it after you. We write Python like software engineers, not data scientists.
• You've deployed and operated ML systems in production: model serving, monitoring, failure handling, and the operational rigor that keeps them running
• You identify the most important technical work and go after it - you've shaped technical roadmaps, influenced peers and organizational direction, and moved goals forward with or without explicit direction
• You define the problem as much as you solve it - you thrive in a team where requirements evolve as you learn, and you see that as a feature, not a bug
• You've worked with real-world messy data: device logs, behavioral data, event streams, or similar

Strong preferences:

• Advanced degree in a quantitative or analytical field - PhDs are very welcome, we already have three
• Intellectual background outside computer science is genuinely valued here: statistics, physics, engineering, biology, economics, linguistics, philosophy - it doesn't have to be a "hard science." We hire for intellectual diversity because it makes the work better.
• Hands-on experience with ML production tooling: model registry, serving infrastructure, pipeline orchestration, and model monitoring
• Experience with cloud data platforms in an ML context (Azure ML, Databricks, Snowflake) and batch or streaming pipeline architecture
• Experience with hardware-adjacent data: device telemetry, IoT event logs, or similar

• 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
• Learning & Development programs
• Employee Resource Groups (ERGs)
• And yes, we have snacks in our offices

Benefits listed herein may vary depending on the nature of your employment and the location where you work.