Position: Data Science Principal Seattle, Washington, United States Seattle, Washington
Location:
Seattle, Washington, United States
Join Axon and be a Force for Good.
Your Impact
The other half is strategic: partnering across Controllership, Finance Operations, Financial Planning and Analysis (FP&A), Internal Audit, and Technology to establish the data foundation that makes AI, automation, and audit readiness possible. You will use AI‑assisted development tools (code generation, copilots, large language models) as part of your everyday build process.
What You’ll Do
Hands‑On Building & Upstream Data Cleanliness (~50%)
- Design and develop data analytics tools, automated validation frameworks, and dashboards that give Finance Operations and Controllership real‑time visibility into data health across enterprise systems. Use AI to rapidly prototype, test, and deploy these solutions.
- Drive upstream data hygiene cross‑functionally: partner with source‑system owners in Revenue Operations, Procurement, Human Resources, and Engineering to fix data issues where they originate, not after they propagate.
- Own data dictionaries, lineage documentation, and quality scorecards for critical controllership data assets. Establish and enforce master data management (MDM) standards.
- Partner with the Chief Information Officer (CIO) and Information Technology (IT) on integration strategy, ensuring seamless data flows between ERP, sub‑ledgers, and the enterprise data platform. Support Snowflake data warehouse deployment.
Mergers & Acquisitions Data Integration
- Lead data work streams for M&A transactions end‑to‑end: perform data due diligence on target companies, assess data quality risks, and build integration playbooks for onboarding acquired‑company data into Axon’s enterprise platform.
- Design and execute data mapping, cleansing, and migration plans for post‑acquisition integration—ensuring acquired entities are reporting on clean, consolidated data within defined timelines.
- For interim stage prior to M&A integration, build, own, and operate extract‑transform‑load (ETL) and extract‑load‑transform (ELT) pipelines that remediate data quality issues at the source—before data reaches downstream AI models, reporting, or analytics. Leverage AI‑assisted development tools (copilots, code generation, large language models) to accelerate pipeline development and iteration.
- Build repeatable M&A controllership data integration frameworks—using AI to automate data profiling, matching, and anomaly detection—that scale as Axon’s acquisition pace accelerates.
Financial Controls, SOX & Audit Readiness
- Co‑design data pipelines and analytics with auditability built in—traceable lineage, complete audit trails, and evidence that holds up under external audit.
- Build or collaborate with process owners to build close‑support tools (GL‑to‑sub‑ledger, intercompany, journal entries) that reduce manual effort and strengthen control evidence.
Enabling AI Builders & Process Owners
- Serve as the data enablement partner to Axon’s Controllership AI team and Controllership process owners—ensuring they have access to clean, well‑structured, governed datasets for machine learning (ML), automation, and large language model (LLM) initiatives.
- Collaborate with AI builders to define data requirements, validate input data quality, and create feedback loops that continuously improve data upstream of AI/ML workflows.
- Create self‑service analytics capabilities that enable FP&A, Tax, Treasury, and Controllership teams to access trustworthy data without bottlenecks.
Partnership & Cross‑Functional Leadership
- Serve as a trusted partner to the CIO, shaping technology roadmaps, enterprise architecture, and system integration decisions. Act as a strong ally to Controllership and Finance Operations.
- Engage stakeholders across FP&A, Tax, Treasury, Engineering, Product, Revenue Operations, and Legal to manage cross‑functional data projects end‑to‑end. Influence without authority across global, distributed teams.
What You Bring
- Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Economics, Finance, or a related field with 8+ years in data science, data engineering, or analytics.
- Hands‑on builder: strong, current proficiency with Structured Query Language…