Senior Director, Finance AI SME, FP&A and Forecasting Transformation - Eden Prairie, MN preferred, Remote considered

Overview

This role serves as a senior finance subject matter expert embedded within Finance AI transformation pods. You will partner with Finance, Data, Technology, Risk and Controls, and business stakeholders to blueprint current-state and future-state finance processes, translate business requirements into agent-ready workflows, and help develop proof-of-concept AI systems that can be validated, governed, and scaled.

The position is designed for a senior finance leader with deep FP&A and forecasting experience who can bridge business finance needs and technical delivery teams. You will bring expertise in enterprise forecasting, variance analysis, financial planning, driver-based modeling, performance reporting, and executive decision support.

Responsibilities

Finance SME Leadership in Pod Delivery

• Serve as a senior Finance SME within cross-functional Finance AI pods focused on FP&A, forecasting, performance reporting, and decision intelligence
• Partner with pod leads, business finance leaders, data teams, solution architects, and engineering resources to define the business problem, target outcomes, scope, requirements, and success criteria for each proof of concept (POC)
• Bring practical FP&A judgment to AI design decisions to ensure prototypes reflect real forecasting, analysis, explanation, and decision-making practices
• Translate business needs into clear, testable requirements for AI-enabled workflows
• Help ensure pod work focuses on high-value finance problems rather than disconnected technology experimentation

Current-State Blueprinting

• Lead structured blueprinting sessions with Finance, business, data, and technology stakeholders to map current-state forecasting and FP&A processes
• Document key process steps, business owners, data sources, systems, handoffs, manual workarounds, spreadsheet dependencies, control points, approvals, recurring pain points, and decision outputs
• Identify root causes of inefficiency, including fragmented data, inconsistent definitions, duplicative reporting, manual variance analysis, delayed business driver inputs, and legacy planning practices
• Distinguish between work that is essential judgment, work that can be automated, work that can be redesigned, and work that should be eliminated
• Partner with Risk and Controls to identify current control dependencies and determine where future-state AI processes must preserve or strengthen auditability, traceability, and human review

Future-State Design and Agent-Ready Workflow Development

• Design future-state FP&A and forecasting workflows incorporating AI agents, governed data products, certified business definitions, automated insight generation, and human-in-the-loop decision rights
• Help define how evergreen forecasting, driver-based forecasting, variance explanation, scenario planning, and executive insight generation should operate across business segments
• Convert process redesign concepts into agent-ready workflows with clear triggers, inputs, outputs, decision points, exception handling, validation requirements, and escalation paths
• Partner with Data and Technology teams to define required data products, semantic layers, model inputs, output expectations, source citations, lineage needs, and evaluation criteria
• Balance speed and automation with Finance requirements for precision, controls, regulatory readiness, and confidence in financial outputs

AI POC Development and Validation

• Partner with engineering and data teams to develop POCs for AI-enabled forecasting, variance analysis, bridge narratives, management reporting, planning support, and decision intelligence
• Define practical test cases, expected outputs, comparison baselines, and acceptance criteria for AI-generated forecasts, insights, narratives, and recommendations
• Validate prototype outputs against known business results, finance logic, source data, and user expectations
• Identify hallucination risk, inconsistent logic, data gaps, control deficiencies, usability issues, and adoption barriers before POCs move toward scale
• Recommend whether POCs should proceed to additional testing, be refined, be redesigned, be paused, or be transitioned to the AI Factory for productization

FP&A and Forecasting Transformation

• Apply deep expertise in forecasting methods, planning cycles, driver-based modeling, variance analysis, business performance management, and senior executive reporting
• Help redesign forecasting processes to move from static cycle-based planning toward more continuous, signal-based, and business-driver-informed forecasting
• Support development of AI-enabled capabilities across medical trend forecasting, cash forecasting, P&L forecasting, segment forecasting, and performance analytics
• Work with business finance teams to identify where AI can improve speed of insight, forecast accuracy, scenario analysis, root-cause identification, and decision support
• Define how forecasting outputs should be consumed by CFOs, segment leaders, business operators, Investor Relations, Treasury, and other stakeholders

Data, Controls, and Governance

• Partner with Data teams to assess data readiness, data quality, source system limitations, business driver availability, and semantic consistency
• Escalate data gaps that prevent reliable AI-enabled forecasting or financial analysis
• Partner with Risk, Controls, Compliance, Internal Audit, and Finance leadership to ensure future-state AI workflows are explainable, traceable, auditable, and appropriately governed
• Support design of human approval gates, output review standards, source documentation, model validation checkpoints, and ongoing monitoring routines
• Ensure AI-enabled workflows consider SOX, financial reporting precision, regulatory expectations, and enterprise governance standards

Stakeholder Engagement and Adoption

• Build credibility with senior Finance and business leaders by demonstrating solid business judgment and practical finance experience
• Facilitate working sessions with business SMEs, Controllers, FP&A leaders, Technology, Data, and Risk partners
• Translate complex AI and data concepts into clear finance implications for senior stakeholders
• Support change management by helping finance users understand how redesigned workflows will improve speed, insight, quality, and capacity
• Capture adoption barriers, training needs, process changes, and role impacts as AI-enabled workflows are piloted and scaled

Required Qualifications

• Bachelor’s degree in Finance, Accounting, Economics, Business, Data Analytics, or a related field
• 12+ years of progressive finance experience, with significant depth in FP&A, forecasting, business performance management, or enterprise financial planning
• Demonstrated experience leading or materially contributing to forecasting, planning, variance analysis, management reporting, or decision support in a large, complex enterprise
• Proven solid understanding of driver-based forecasting, P&L analysis, business metrics, executive reporting, and financial planning processes
• Experience working across business, finance, technology, and data teams to deliver process improvement, automation, analytics, or transformation initiatives
• Ability to map complex current-state processes and translate them into simplified future-state operating models
• Proven solid analytical and problem-solving skills, including evaluating data quality, process gaps, business logic, and control implications
• Proven solid executive communication skills, including the ability to synthesize complex issues, frame trade-offs, and make clear recommendations
• Ability to operate in ambiguity, influence without direct authority, and move work forward across a matrixed organization
• Proven high degree of ownership, urgency, judgment, and attention to detail

Preferred Qualifications

• MBA, CPA, CMA, or equivalent advanced finance, accounting, or analytics credential
• Experience with AI, machine learning, automation, analytics products, data products, or digital finance transformation
• Experience with Oracle EPM, enterprise planning tools, data visualization platforms, financial data marts, Snowflake, Azure, or related enterprise data environments
• Experience designing or implementing driver-based forecasting, rolling forecasts, scenario modeling, or advanced business performance analytics
• Experience supporting finance transformation in a regulated environment with SOX, SEC reporting, audit, model governance, or other control considerations
• Familiarity with agentic AI concepts, LLM-enabled workflows, human-in-the-loop controls, retrieval-augmented generation, prompt evaluation, and AI output validation
• Experience working in a product-oriented or agile delivery model, including POCs, pilots, MVPs, product roadmaps, and adoption measurement

Compensation & Benefits

• Salary range: $159,300 - $273,200 annually based on full-time employment
• Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc.
• Benefits include a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements)

Location

• Remote: You may work remotely from anywhere within the U.S.
• For 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.

Additional Information

• Application deadline: Posted for a minimum of 2 business days or until a sufficient candidate pool has been collected; the posting may come down early due to volume of applicants.
• Remote employees are required to adhere to the employer’s Telecommuter Policy.
• Candidates are required to pass a drug test before beginning employment.