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United States \- New York
Product Management and Development
Global Wealth Management
Job Reference \#
331525BR
City
New York
Job Type
Full Time
Your role
Revolutionize Wealth Management with AI
We’re building modern, world\-class AI products that help our Wealth Management advisors serve clients better. You will be responsible for executing our AI strategy and ideating in the rapidly evolving environment of enterprise AI. This is inclusive of chatbots as well as agentic solutions all with the goal of “codifying investment advice” for our advisors and saving them time. You’ll own outcomes end to end, lead people, and partner closely with Design, Engineering, Data Science, and Product Marketing to ship experiences advisors love.
What you’ll do:
- Own an AI product portfolio end to end: define strategy, shape roadmaps, and deliver advisor facing capabilities (e.g., productivity tools, advisor workflows, insights \& analytics, chat/assistive tools).
- Define products \& features: Develop and maintain technical requirements, user stories, and design documents for product features.
- Design delightful experiences: partner with UX to drive elegant, usable flows; insist on research and measurable UX outcomes
- Ship with quality and compliance: collaborate with Engineering, Data Science, Risk, Legal, and Compliance to meet regulatory and supervisory expectations in wealth management.
- Lead and develop people: hire, coach, and performance manage PMs/POs (and/or cross functional squads); raise the bar on product craft.
- Tell the story: translate complex concepts into crisp narratives for senior stakeholders; influence prioritization with data (adoption, CSAT, etc).
- Continuously improve: instrument, test, learn; run experiments and iterate to drive advisor productivity and client outcomes.
- Analyze trends: Analyze user behavior, market trends, and competitor activity to inform product decisions.
- Scale operating rigor: own backlogs, OKRs, metrics, release readiness, and business change (training, comms, enablement).
Your Career Comeback
We are open to applications from career returners. Find out more about our program on ubs.com/careercomeback.
Your team
You’ll join the STAAT Field Solutions crew within Data, Analytics \& Foundational Platforms, focused on giving Financial Advisors modern tools and insights to grow and serve clients.
Your expertise
You’re a great fit if you have
- 12\+ years in Product Management (or adjacent), with deep financial services experience — ideally Wealth Management / private banking
- Experience building AI powered products (e.g., chatbots, predictive analytics, personalization engines).
- 2\+ years of people leadership (managing PMs or multi disciplinary squads) with a track record developing talent.
- A UX/Design mindset: portfolio of shipped experiences that are intuitive, accessible, and visually polished; you can point to measurable UX impact.
- Fluent PM craft: discovery through delivery, structured prioritization, evidence based decisions, crisp problem statements, and strong execution habits.
- Strong stakeholder \& exec communication in a complex, regulated environment.
- Technical and data literacy: you’re great with APIs, data models, and platform constraints; comfortable partnering with engineers and data scientists.
Nice to have
- Experience with advisor desktop/workstation, CRM, portfolio reporting, or client communications tooling.
- Design thinking / service design, or prior hands on research experience.
About us
UBS is a leading and truly global wealth manager and the leading universal bank in Switzerland. We also provide diversified asset management solutions and focused investment banking capabilities. Headquartered in Zurich, Switzerland, UBS is present in more than 50 markets around the globe.
We know that great work is never done alone. That’s why we place collaboration at the heart of everything we do. Because together, we’re more than ourselves. Want to find out more? Visit ubs.com/careers.
Salary information
The indicative gross base salary range as a full\-time equivalent role:
- United States \- New York \- New York min USD 160000 \- max USD 250000 /annum
The expected salary for this role will be determined by relevant factors which may include but are not limited to, role\-required experience, qualifications, education, location and skill level. UBS offers a range of competitive benefits and for further information, please visit ubs.com/employee\-benefits. We may, at our sole discretion, provide additional variable compensation or awards.
Join us
At UBS, we know that it's our people, with their diverse skills, experiences and backgrounds, who drive our ongoing success. We’re dedicated to our craft and passionate about putting our people first, with new challenges, a supportive team, opportunities to grow and flexible working options when possible. Our inclusive culture brings out the best in our employees, wherever they are on their career journey. And we use artificial intelligence (AI) to work smarter and more efficiently. We also recognize that great work is never done alone. That’s why collaboration is at the heart of everything we do. Because together, we’re more than ourselves.
We’re committed to disability inclusion and if you need reasonable accommodation/adjustments throughout our recruitment process, you can always contact us.
Disclaimer / Policy statements
UBS is an Equal Opportunity Employer. We respect and seek to empower each individual and support the diverse cultures, perspectives, skills and experiences within our workforce.
Salary Context
This $160K-$250K range is above the median for AI Product Manager roles in our dataset (median: $189K across 161 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 3,823 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At UBS, 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 $213,800 based on 583 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. Disclosed range: $160K to $250K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
UBS AI Hiring
UBS has 4 open AI roles right now. They're hiring across Data Scientist, AI Product Manager. Based in New York, NY, US. Compensation range: $93K - $250K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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