Data Science Principal

Axon Axon · Enterprise · Boston, MA · 4506 Revenue Accounting & Commercial Controllership

This role focuses on building data analytics tools, pipelines, and frameworks to ensure data quality, governance, and audit readiness for AI builders and finance/accounting operations. It involves hands-on coding in SQL and Python, leveraging AI as a development accelerator, and partnering across departments for data integration, remediation, and enablement of AI/ML initiatives within a finance and accounting context. The role also supports M&A data integration and SOX compliance.

What you'd actually do

  1. 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.
  2. 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.
  3. Lead data workstreams 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.
  4. 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.
  5. Co-design data pipelines and analytics with auditability built in—traceable lineage, complete audit trails, and evidence that holds up under external audit.

Skills

Required

  • SQL
  • Python
  • data analytics tools
  • automated validation frameworks
  • dashboards
  • data hygiene
  • data dictionaries
  • lineage documentation
  • quality scorecards
  • master data management (MDM)
  • data governance
  • M&A data integration
  • data due diligence
  • data mapping
  • data cleansing
  • data migration
  • ETL/ELT pipelines
  • data profiling
  • anomaly detection
  • audit trails
  • data lineage
  • self-service analytics

Nice to have

  • AI-assisted development tools
  • code generation
  • copilots
  • large language models
  • Snowflake data warehouse

What the JD emphasized

  • hands-on builder
  • deep finance and accounting domain expertise
  • clean, trusted, well-governed, and audit-ready data
  • upstream data hygiene
  • data dictionaries, lineage documentation, and quality scorecards
  • master data management (MDM) standards
  • data due diligence
  • data quality risks
  • data mapping, cleansing, and migration plans
  • data quality issues
  • auditability built in
  • traceable lineage
  • complete audit trails
  • clean, well-structured, governed datasets for machine learning (ML), automation, and large language model (LLM) initiatives
  • input data quality
  • trustworthy data

Other signals

  • AI as a development accelerator
  • AI-assisted development tools
  • data foundation that makes AI, automation, and audit readiness possible
  • enabling AI builders
  • data upstream of AI/ML workflows