Senior Director, Finance AI SME,, Record-to-Report Transformation - Eden Prairie, MN preferred, Remote

UnitedHealth Group•Eden Prairie, MN

3d•$159,300 - $273,200•Remote

About The Position

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 strong 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. 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.

Requirements

Bachelor’s degree in Accounting, Finance, Business, Economics, Information Systems, or a related field
10+ years of progressive accounting, controllership, audit, financial reporting, finance transformation, or R2R experience
Big 4 public accounting experience required, 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
Solid understanding of accounting controls, financial statement assertions, auditability, evidence requirements, and external auditor expectations
Proven ability to map complex current-state processes and translate them into simplified, controlled, future-state operating models
Proven solid analytical and problem-solving skills, with the ability to evaluate process gaps, data issues, accounting logic, control implications, and operational risks
Proven solid executive communication skills, including the ability to synthesize complex accounting and technology topics into clear recommendations
Proven ability to operate in ambiguity, influence across a matrixed organization, and move work forward with urgency and discipline

Nice To Haves

CPA
Experience in controllership, corporate accounting, SEC reporting, technical accounting, statutory reporting, consolidation, or accounting policy
Experience with large enterprise ERP, consolidation, reconciliation, planning, reporting, workflow, or close management systems
Experience with PeopleSoft, Oracle, SAP, BlackLine, OneStream, Workiva, Snowflake, Azure, Power BI, or similar enterprise finance/data platforms
Experience leading finance transformation, close acceleration, global process ownership, shared services, automation, or digital accounting programs
Experience working in a regulated environment with SOX, SEC reporting, NAIC statutory reporting, external audit, model governance, regulatory exams, or Internal Audit involvement
Experience working in a product-oriented or agile delivery model, including POCs, pilots, MVPs, product roadmaps, acceptance criteria, testing protocols, and adoption measurement
Familiarity with AI, machine learning, agentic AI, LLM-enabled workflows, retrieval-augmented generation, prompt evaluation, human-in-the-loop controls, and AI output validation

Responsibilities

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
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
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
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
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
Apply discipline to the design, testing, and governance of AI-enabled R2R processes
Bring a strong 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
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
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