Interested in this AI/ML Engineer role at UnitedHealth Group?
Apply Now →Skills & Technologies
About This Role
UnitedHealth Group is a health care and well-being company that's dedicated to improving the health outcomes of millions around the world. We are comprised of two distinct and complementary businesses, UnitedHealthcare and Optum, working to build a better health system for all. Here, your contributions matter as they will help transform health care for years to come. Make an impact with a team that shares your passion for helping others. Join us in making healthcare work better for everyone through people-led, responsible AI while Caring. Connecting. Growing together.
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
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:
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 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
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 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
Future-State Design and Agent-Ready Workflow Development
- 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
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
- 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
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 are built with appropriate consideration for 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, 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
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 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
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
- All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy.
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $159,300 - $273,200 annually based on full-time employment. We comply with all minimum wage laws as applicable.
Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.
At UnitedHealth Group, our mission is to help people live healthier lives and help make the health system work better for everyone. Together, we are shaping the future of healthcare by harnessing technology and innovation to make care simpler to navigate, more affordable and more connected for the people we serve. We are committed to creating an inclusive workplace where everyone feels welcomed, valued, heard and respected, empowering people to bring their authentic selves to work and strengthening our collective impact through diverse talents, backgrounds, experiences and perspectives.
UnitedHealth Group and its affiliated brands are an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations.
UnitedHealth Group and its affiliated brands are a drug-free workplace. Candidates are required to pass a drug test before beginning employment.
Salary Context
This $159K-$273K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At UnitedHealth Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. Disclosed range: $159K to $273K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
UnitedHealth Group AI Hiring
UnitedHealth Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Eden Prairie, MN, US. Compensation range: $273K - $273K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.