Data Science Principal

Axon•Boston, MA

About The Position

Join Axon and be a Force for Good. 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 each other. 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. Your Impact Axon Enterprise (Nasdaq: AXON) is seeking a Data Science Principal within the Office of the Chief Accounting Officer (CAO). This is a hands-on enabler role for a technically exceptional professional with deep finance and accounting domain expertise. Your primary mission is to ensure that Axon’s AI builders, process owners, decision-makers, and auditors can operate with clean, trusted, well-governed, and audit-ready data. You will spend roughly half your time building data analytics tools and pipelines—leveraging AI as a development accelerator—to support complex, large-scale projects including mergers and acquisitions (M&A) integration, enterprise resource planning (ERP) system deployments, upstream data remediation, and SOX compliance initiatives. 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.

Requirements

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 (SQL) and Python—you write code, build pipelines, and ship tools, not just direct others. Comfortable using AI-assisted development tools to multiply your output.
Deep experience with upstream data quality remediation, master data management, and data governance operationalization in a finance or accounting environment.
Demonstrated M&A data integration experience: due diligence, data mapping, migration, and post-acquisition consolidation.
Ability to build senior-level relationships, lead cross-functional projects end-to-end, and influence without direct authority across global teams.
Working knowledge of US GAAP accounting processes (R2R, O2C, P2P, revenue recognition) or FP&A processes (budgeting, forecasting, variance analysis)—you understand the business logic, not just the data.
Practical familiarity with SOX compliance and IT General Controls (ITGCs) sufficient to design audit-ready data pipelines, maintain traceable audit trails, and support internal audit teams and external auditors with data extracts, population listings, and control evidence—without needing to own the SOX program or control design.

Nice To Haves

Snowflake or comparable cloud data warehouse experience. Business intelligence (BI) platforms such as Sigma.
High-growth, publicly traded technology company or digital transformation environment.
Experience enabling AI/ML teams with clean data pipelines. Demonstrated use of AI code-generation in day-to-day development. Supervisory experience leading data teams.
M&A integration at a company with an active acquisition strategy. Public safety, defense, or regulated industry background.
Experience with audit management or financial close platforms/ERP and their data integration requirements; familiarity with how these tools consume and produce audit evidence.

Responsibilities

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.
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.
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.
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.
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.
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.
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.