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

UnitedHealth Group•Eden Prairie, MN

2d•$159,300 - $273,200•Remote

About The Position

The Senior Director, Finance AI SME, FP&A and Forecasting Transformation will serve as a senior finance subject matter expert embedded within Finance AI transformation pods. This leader will partner closely 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. This role is designed for a senior finance leader with deep FP&A and forecasting experience who can bridge the gap between business finance and technical delivery teams. The individual will bring practical expertise in enterprise forecasting, variance analysis, financial planning, driver-based modeling, performance reporting, and executive decision support. The role requires the ability to understand how finance work is performed today, identify where AI and automation can materially improve speed, quality, controls, and insight, and help convert future-state designs into tangible AI-enabled solutions. 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, and business finance leaders. The person will not simply advise from the sidelines. They will be expected to lead hands-on blueprinting sessions, define use-case requirements, pressure-test data and control assumptions, validate prototype outputs, and ensure that AI systems are solving real finance problems in a way that is accurate, explainable, auditable, and adoptable. The purpose of this role is to provide senior-level FP&A and forecasting expertise to Finance AI pods that are redesigning core finance processes and developing proof-of-concept AI systems. The Senior Director will help move Finance from periodic, manual, and fragmented forecasting and analysis processes toward more continuous, driver-based, data-enabled, and AI-supported decision intelligence. The role will focus on three integrated mandates: Blueprint the current state. Understand and document existing FP&A and forecasting activities, including process flows, data inputs, handoffs, manual workarounds, estimation methods, controls, outputs, and decision points. Design the future state. Partner with business and technology stakeholders to define how forecasting, variance analysis, scenario modeling, performance reporting, and decision support should operate when AI, data, controls, and human judgment are designed together from the beginning. Develop and validate AI proof-of-concept systems. Translate finance requirements into clear prototype designs, partner with data and technology teams during development, validate outputs against business expectations, and help determine whether POCs should be killed, refined, scaled, or transitioned to the AI Factory for productization.

Requirements

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, with the ability to evaluate 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

Nice To Haves

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

Responsibilities

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 POC
Bring practical FP&A judgment to AI design decisions, ensuring prototypes reflect how finance leaders actually forecast, analyze, explain, and make decisions
Act as the translator between finance users and technical teams, converting business needs into clear, testable requirements for AI-enabled workflows
Help ensure pod work remains focused on high-value finance problems rather than disconnected technology experimentation
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 the 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
Design future-state FP&A and forecasting workflows that incorporate 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
Ensure future-state designs balance speed and automation with Finance’s unique requirements for precision, controls, regulatory readiness, and confidence in financial outputs
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
Bring 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
Help define how forecasting outputs should be consumed by CFOs, segment leaders, business operators, Investor Relations, Treasury, and other stakeholders
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 are built with appropriate consideration for SOX, financial reporting precision, regulatory expectations, and enterprise governance standards
Build credibility with senior Finance and business leaders by demonstrating solid business judgment, practical finance experience, and command of forecasting and performance management
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