The Senior Director, Finance AI SME, Record-to-Report Transformation will serve as a senior controllership and accounting subject matter expert embedded within Finance AI transformation pods. This leader will partner with Finance, Controllership, Data, Technology, Risk and Controls, Internal Audit, and business stakeholders to blueprint current-state and future-state record-to-report processes and help develop proof-of-concept AI systems that can be validated, governed, and scaled.
This role is designed for a senior accounting and controllership leader with a solid public accounting foundation and deep experience across the financial close, journal entries, reconciliations, consolidations, intercompany, external reporting, statutory reporting, technical accounting, SOX/ICFR, and audit readiness. The person will bring the judgment required to determine where AI can safely automate or augment record-to-report work, where human review remains non-negotiable, and how future-state processes should be designed to improve speed, quality, traceability, and control.
The Senior Director will operate in a cross-functional pod model, working side by side with data engineers, solution architects, forward deployed engineers, AI/ML resources, risk and controls partners, accounting leaders, and business finance teams. This is a hands-on transformation role. The individual will be expected to lead blueprinting sessions, define R2R process requirements, identify control dependencies, validate prototype outputs, challenge data and accounting logic, and help determine whether proof-of-concept systems are ready to be refined, scaled, or transitioned into the AI Factory for productization.
The ideal candidate combines audit discipline, enterprise controllership experience, process redesign capability, and practical curiosity about AI. They must be able to translate accounting and reporting requirements into agent-ready workflows that are accurate, auditable, explainable, and acceptable to Finance leadership, Internal Audit, external auditors, and regulators.
Role Purpose
The purpose of this role is to provide senior-level R2R expertise to Finance AI pods that are redesigning core accounting and reporting processes and developing proof-of-concept AI systems. The Senior Director will help move Finance from manual, calendar-driven, spreadsheet-heavy close processes toward a more continuous, exception-based, data-enabled, and AI-supported controllership model.
The role has three integrated mandates:
- Blueprint the current state. Understand and document how R2R activities operate today across journal entries, reconciliations, close management, intercompany, consolidation, reporting, technical accounting, statutory reporting, controls, data flows, and audit evidence
- Design the future state. Partner with Finance, Technology, Data, Risk, and business stakeholders to define how R2R should operate when people, AI agents, governed data, controls, and accounting judgment are designed together from the beginning
- Develop and validate AI proof-of-concept systems. Translate accounting requirements into clear prototype designs, help build and test R2R agents, validate outputs against accounting standards and source data, and determine whether solutions are ready for additional testing, productization, or scale
You'll enjoy the flexibility to work remotely * from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.
Primary Responsibilities:
R2R Finance SME Leadership in Pod Delivery
- Serve as a senior Finance SME within cross-functional Finance AI pods focused on record-to-report, controllership, close, consolidation, and reporting transformation
- Partner with pod leads, accounting leaders, business finance teams, data teams, technology teams, and risk partners to define the business problem, target outcomes, scope, requirements, and success criteria for R2R POCs
- Bring practical controllership judgment to AI design decisions, ensuring prototypes reflect how accounting work is actually performed, reviewed, approved, reconciled, reported, and audited
- Translate accounting and reporting requirements into buildable, testable, agent-ready workflows for data engineers, solution architects, and AI/ML teams
- Help ensure pod work remains focused on high-value R2R issues, including close-cycle compression, control effectiveness, reconciliation automation, reporting quality, audit readiness, and risk reduction
Current-State Blueprinting
- Lead structured blueprinting sessions with Controllership, Accounting, SEC Reporting, Statutory Reporting, Technical Accounting, Finance Systems, Data, Technology, Risk, and Internal Audit stakeholders
- Document current-state R2R processes, including key process steps, systems, data inputs, subledger feeds, manual workarounds, spreadsheet dependencies, handoffs, approvals, review controls, close calendar dependencies, reporting outputs, and audit evidence
- Identify fragmentation across journals, reconciliations, intercompany, eliminations, consolidations, external reporting, statutory reporting, and management reporting
- Assess where manual activities are driven by data quality issues, inconsistent definitions, system limitations, control design, historical practices, or lack of end-to-end ownership
- Distinguish between activities requiring accounting judgment, activities suitable for AI-assisted preparation, activities suitable for automation, and activities that should be simplified or eliminated
- Partner with Risk and Controls to identify current SOX/ICFR dependencies and determine how future-state AI-enabled processes should preserve or strengthen control evidence, segregation of duties, review precision, and auditability
Future-State Design and Agent-Ready Workflow Development
- Design future-state R2R workflows that incorporate AI agents, governed data products, semantic definitions, source-to-report lineage, automated validations, exception routing, and human-in-the-loop decision rights
- Help define how continuous close, automated journal entry support, account reconciliation automation, intercompany matching, consolidation support, flux analysis, reporting narrative generation, and statutory filing support should operate in the future state
- Convert process redesign concepts into agent-ready workflows with clear triggers, inputs, outputs, decision points, approval gates, exception thresholds, source citations, audit trails, and escalation paths
- Partner with Data and Technology teams to define required data products, subledger feeds, general ledger integration needs, knowledge layers, evaluation criteria, and control logs
- Ensure future-state R2R designs are practical for large-scale enterprise accounting environments and are compatible with financial reporting precision, SOX/ICFR expectations, external audit needs, and regulatory requirements
- Define the appropriate division of labor between agents and humans, ensuring agents prepare, reconcile, validate, draft, and monitor, while accountable finance leaders retain final judgment, approval, certification, and external reporting responsibility
AI POC Development and Validation
- Partner with engineering, data, and architecture teams to develop and test POCs for journal entry preparation, journal entry validation, account reconciliations, close calendar monitoring, data fallout resolution, consolidation checks, flux analysis, reporting narratives, statutory reporting support, and technical accounting intake
- Define practical test cases, expected outputs, exception scenarios, comparison baselines, evaluation standards, and acceptance criteria for AI-enabled R2R capabilities
- Validate AI outputs against ledger data, subledger data, accounting policy, source documentation, close checklists, reconciliation standards, reporting requirements, and control expectations
- Identify hallucination risk, unsupported accounting conclusions, inconsistent logic, missing evidence, data lineage gaps, control deficiencies, model drift risks, and usability barriers
- Recommend whether POCs should proceed to additional testing, be refined, be redesigned, be paused, or be transitioned to the AI Factory for productization
- Partner with Risk and Controls to ensure that POCs include appropriate audit logs, issue tracking, exception handling, reviewer sign-off, and evidence retention
Record-to-Report Transformation
- Bring deep knowledge of the R2R lifecycle, including transaction capture, journal entries, account reconciliations, accruals, estimates, intercompany, eliminations, consolidation, financial statement close, management reporting, GAAP/SEC reporting, statutory reporting, and technical accounting
- Help redesign close processes to move from periodic, manual, task-based execution toward continuous, exception-based, data-enabled controllership
- Identify opportunities to reduce manual close activities, eliminate duplicative controls, standardize recurring accounting logic, improve reconciliation quality, and shorten close cycle time
- Support design of AI-enabled capabilities such as journal entry agents, reconciliation agents, close calendar agents, data feed validation agents, consolidation agents, flux/variance agents, report drafting agents, and statutory filing agents
- Help define how the R2R function can produce earlier, more reliable, and more explainable financial outputs for controllers, CFOs, business leaders, Internal Audit, external auditors, and regulators
Controls, and Audit Readiness
- Apply discipline to the design, testing, and governance of AI-enabled R2R processes
- Bring a solid understanding of audit evidence, audit trails, management review controls, control precision, financial statement assertions, risk assessment, materiality, and external auditor expectations
- Partner with SOX/ICFR, Risk and Controls, Internal Audit, and external audit stakeholders to ensure AI-enabled processes can support reliable financial reporting
- Help determine when AI output can be used as a productivity aid versus when it may become part of control evidence or management review support
- Ensure appropriate human approval gates are embedded for high-risk journal entries, accounting estimates, reserves, technical accounting conclusions, disclosures, material eliminations, consolidation matters, and filing certifications
- Support control design for AI-enabled workflows, including segregation of duties, maker-checker principles, evidence capture, reviewer comments, approval history, prompt/output logging, source citations, and exception reporting
Data, Systems, and Governance
- Partner with Data and Technology teams to assess R2R data readiness, including general ledger, subledger, consolidation, reconciliation, reporting, statutory, and supporting operational data sources
- Identify data quality, connectivity, lineage, completeness, timing, and semantic definition gaps that must be addressed before AI-enabled R2R processes can scale
- Help define the knowledge layers and approved-source retrieval structures required for accounting policies, close procedures, reporting checklists, precedent decisions, statutory rules, and control documentation
- Partner with enterprise governance teams to ensure R2R AI solutions meet responsible AI, model governance, information security, data retention, privacy, and financial reporting requirements
- Support development of reusable control templates, evaluation standards, evidence packages, and onboarding playbooks for R2R AI capabilities
Stakeholder Engagement and Adoption
- Build credibility with controllers, accounting leaders, business finance teams, audit partners, technology teams, and executive stakeholders
- Facilitate working sessions that bring structure to complex R2R pain points and translate those issues into practical AI-enabled workflow requirements
- Communicate the role of AI in R2R clearly, emphasizing that agents can prepare, validate, reconcile, draft, and monitor, but humans remain accountable for accounting judgments, approvals, certifications, and external reporting positions
- Support change management by helping accounting teams understand how future-state workflows will improve speed, quality, consistency, and control
- Capture adoption barriers, training needs, role impacts, and operating model changes as POCs are piloted and scaled
- Help establish repeatable playbooks so validated R2R capabilities can be extended across business units, market groups, legal entities, and reporting bases
You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.
Required Qualifications:
- Bachelor's degree in Accounting, Finance, Business, Economics, Information Systems, or a related field
- CPA
- 10+ years of progressive accounting, controllership, audit, financial reporting, finance transformation, or R2R experience
- Big 4 public accounting experience, preferably with audit experience serving large, complex, publicly traded or regulated companies
- Significant experience with financial close, journal entries, account reconciliations, consolidation, intercompany, external reporting, SOX/ICFR, audit evidence, and management review controls
- Demonstrated ability to lead or materially contribute to process redesign, operating model transformation, automation, financial systems, or accounting modernization initiatives
- Proven solid understanding of
