Interested in this AI Product Manager role at U.S. Bank?
Apply Now →About This Role
At U.S. Bank, we’re on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed. We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bank gives you a wide, ever\-growing range of opportunities to discover what makes you thrive at every stage of your career. Try new things, learn new skills and discover what you excel at—all from Day One.
Job Description
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Product Managers at U.S. Bank are customer obsessed in driving product visioning, planning and development to deliver on our human \+ digital product strategy and staying one step ahead of evolving economic, technological, and customer needs.
As a Product Manager, you will:
- Identify, analyze and interpret business data using various kinds of techniques to meet business needs and requirements.
- Activate the connective tissue between U.S. Bank, customer experience, digital, technology, and business line strategy to identify and prioritize product / portfolio opportunities, and establish the product vision, charter and roadmap.
- Defines product outcomes and Objectives and Key Results (OKRs) to prioritize Features/Epics and breaks down work into actionable steps to facilitate product discovery, design, development and delivery.
- Manages the end\-to\-end product lifecycle and drives the product roadmap leveraging OKRs (Objectives and Key Results). Creates the product in conjunction with agile, experience design, technology and risk partners to achieve business and customer outcomes. In the case of technical products, understands and cares for the technical features, debt reduction and modernization of the technical product with expertise in the technology required of the product.
Preferred qualifications, capabilities, and skills:
- Expertise on the critical interdependencies among product model system elements that help and hinder performance; ability to plan and design solutions that synchronize resources to achieve business results using business metrics, key performance indicators, design systems, service blueprints, and reusability.
- Ability to anticipate shifting market priorities, internal and external customer needs in a manner that consistently adds value.
- Strong customer centricity to implement strategies and techniques used to ensure that customers have a positive experience with the organization's products and services at every touch point.
- Knowledge of technical aspects of software products; ability to design, configure and integrate technical aspects of software products.
- Knowledge of Agile ways of working, knowledge of processes, tools and techniques for assessing and controlling an organization's exposure to risks of various kinds; ability to apply knowledge of risk management appropriately to diverse situations.
- Preferred skills: product PnL, product strategy, vision and planning, product discovery, product development, channel alignment and management, adoption, customer experience research, insight and execution, agile ways of working, go to market and sales channels, performance measurement and optimization, marketing and analytics, customer centricity.
- Experience in mortgage lending, home equity, or consumer lending operations.
- Experience leading AI, machine learning, intelligent automation, or digital transformation initiatives.
- Familiarity with AI governance, model risk management, regulatory compliance, and data governance frameworks
Minimum Qualifications:
- Product management experience.
About the team/product (s):
The Senior Product Manager leads the strategy, development, and delivery of AI\-enabled solutions that enhance the Mortgage and Home Equity origination experience. This role partners across business, technology, data science, operations, risk, and governance teams to identify, evaluate, and implement AI capabilities that improve customer outcomes, operational efficiency, and business performance. Responsibilities include defining product roadmaps, developing business cases, translating business needs into scalable solutions, overseeing implementation, ensuring compliance with governance and regulatory requirements, driving adoption across the organization, and measuring the impact of AI initiatives to support ongoing innovation and value realization.
Location expectations
This role requires working from a U.S. Bank location three (3\) or more days per week.
- This role does not support Visa sponsorship
If there’s anything we can do to accommodate a disability during any portion of the application or hiring process, please refer to our disability accommodations for applicants.
Benefits:
Our approach to benefits and total rewards considers our team members’ whole selves and what may be needed to thrive in and outside work. That's why our benefits are designed to help you and your family boost your health, protect your financial security and give you peace of mind. Our benefits include the following:
- Healthcare (medical, dental, vision)
- Basic term and optional term life insurance
- Short\-term and long\-term disability
- Pregnancy disability and parental leave
- 401(k) and employer\-funded retirement plan
- Paid vacation (from two to five weeks depending on salary grade and tenure)
- Up to 11 paid holiday opportunities
- Adoption assistance
- Sick and Safe Leave accruals of one hour for every 30 worked, up to 80 hours per calendar year unless otherwise provided by law
Review our full benefits available by employment status here.
U.S. Bank is an equal opportunity employer. We consider all qualified applicants without regard to race, religion, color, sex, national origin, age, sexual orientation, gender identity, disability or veteran status, and other factors protected under applicable law.
E\-Verify
U.S. Bank participates in the U.S. Department of Homeland Security E\-Verify program in all facilities located in the United States and certain U.S. territories. The E\-Verify program is an Internet\-based employment eligibility verification system operated by the U.S. Citizenship and Immigration Services.
The salary range reflects figures based on the primary location, which is listed first. The actual range for the role may differ based on the location of the role. In addition to salary, U.S. Bank offers a comprehensive benefits package, including incentive and recognition programs, equity stock purchase 401(k) contribution and pension (all benefits are subject to eligibility requirements). Pay Range: $126,820\.00 \- $149,200\.00
U.S. Bank will consider qualified applicants with arrest or conviction records for employment. U.S. Bank conducts background checks consistent with applicable local laws, including the Los Angeles County Fair Chance Ordinance and the California Fair Chance Act as well as the San Francisco Fair Chance Ordinance. U.S. Bank is subject to, and conducts background checks consistent with the requirements of Section 19 of the Federal Deposit Insurance Act (FDIA). In addition, certain positions may also be subject to the requirements of FINRA, NMLS registration, Reg Z, Reg G, OFAC, the NFA, the FCPA, the Bank Secrecy Act, the SAFE Act, and/or federal guidelines applicable to an agreement, such as those related to ethics, safety, or operational procedures.
Applicants must be able to comply with U.S. Bank policies and procedures including the Code of Ethics and Business Conduct and related workplace conduct and safety policies.
Posting may be closed earlier due to high volume of applicants.
Salary Context
This $126K-$149K range is in the lower quartile for AI Product Manager roles in our dataset (median: $188K across 140 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At U.S. Bank, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($138K) sits 36% below the category median. Disclosed range: $126K to $149K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
U.S. Bank AI Hiring
U.S. Bank has 11 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer. Positions span New York, NY, US, Minneapolis, MN, US, Irving, TX, US. Compensation range: $101K - $252K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
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