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About This Role
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Explore your next opportunity at a Fortune Global 500 organization. Envision innovative possibilities, experience our rewarding culture, and work with talented teams that help you become better every day. We know what it takes to lead UPS into tomorrow—people with a unique combination of skill + passion. If you have the qualities and drive to lead yourself or teams, there are roles ready to cultivate your skills and take you to the next level.
Job Description:
THIS POSITION IS HYBRID AND WILL REQUIRE CANDIDATES TO WORK IN THE FOLLOWING UPS IT LOCATIONS: NEW JERSEY, ATLANTA, MARYLAND, AND KENTUCKY OFFICE 3 DAYS A WEEK
Role Overview
We are looking for a high-agency, product-minded Senior Product Manager to own the CommerceShield product family — UPS Digital’s AI-powered risk platform for e-commerce merchants. CommerceShield scores every order before fulfillment by combining identity, device, and payment signals with proprietary UPS delivery, address, claims, and proof-of-delivery intelligence, then gives merchants the ability to act on that risk — hold, route, verify, protect, or approve — and to shift chargeback liability on eligible orders.
This role owns the full family: risk orchestration, checkout protection, and chargeback protection, delivered through multiple connectivity paths including the Shopify app, direct REST API, and the LynkUp integration and orchestration platform. You will be responsible for how merchants experience risk decisioning across each of those paths — what the score means, what actions it triggers, and how quickly a merchant can go from integration to measurable value.
This position sits at the intersection of applied AI, enterprise APIs, e-commerce platforms, and modern SaaS. It is equal parts scaling an early-commercial product and 0 1 new capability development, and it includes leading a small team — 1–2 product analysts plus cross-functional agile partners. We operate on a modern product operating model: outcome-based roadmaps, continuous customer discovery, rapid experimentation, and empowered cross-functional teams. You will be accountable for outcomes — adoption, retention, revenue, and loss reduction for our merchants — not for activity.
Key Responsibilities
Portfolio Strategy and Ownership
- Own the roadmap and end-to-end lifecycle for the CommerceShield family — risk orchestration, checkout protection, and chargeback protection — balancing net-new capability development with scaling what’s already in market.
- Define how the product shows up differently across connectivity paths (Shopify app, REST API/webhook, LynkUp, and platform-native integrations), and make deliberate decisions about where to invest for reach versus depth.
- Translate customer insight, competitive signal, and business priorities into a prioritized, outcome-based roadmap, and represent it credibly in executive, customer, and partner conversations.
AI and Intelligent Decisioning
- Partner with data science, ML, and engineering to evolve the scoring and decisioning engine — from configurable rules and weighted signals toward predictive, model-driven risk intelligence and, over time, agent-assisted and autonomous risk workflows.
- Turn model output into merchant value: define how scores, reason codes, and recommended actions are surfaced so a merchant knows not just how risky an order is, but what to do about it.
- Champion the proprietary-data advantage — work with data and engineering to convert UPS delivery, address, and claims intelligence into durable product differentiation that software-only competitors structurally cannot replicate.
- Establish the disciplines that make AI products trustworthy in production: evaluation frameworks, shadow-mode testing, drift and performance monitoring, guardrails, human-in-the-loop controls, and clear ownership of decision quality.
- Apply responsible-AI practices across the lifecycle — fairness, transparency, explainability, data privacy, security, and appropriate governance for automated decisions that affect real customers.
- Use AI-native ways of working — rapid prototyping, synthetic data, AI-accelerated discovery and analysis — to shorten learning cycles.
Customer Discovery and Experimentation
- Run continuous discovery with merchants, platform partners, and internal sales teams to understand where loss actually occurs and where existing fraud tools stop.
Lead 0* 1 development for new capabilities — problem validation, MVP definition, pilot design, and product-market-fit testing — and use evidence to decide whether to scale, pivot, or stop.
- Design and run experiments on scoring thresholds, orchestration actions, packaging, and merchant-facing experience, and instrument them so results are defensible.
Platform, API, and Partner Integration
- Own the integration and developer experience — onboarding, documentation, reliability, time-to-first-value — across API, app-based, and orchestration-led paths.
- Partner with platform and channel partners (e.g., commerce platforms and marketplaces) to define native experiences that meet merchants where they already work.
- Bring real technical acumen to discussions on API design, latency, signal quality, data privacy, extensibility, and scalability, and make informed tradeoffs with engineering and architecture.
Team Leadership
- Manage, coach, and develop 1–2 product analysts — setting clear priorities, decision rights, and expectations, and giving them meaningful ownership of product areas, analysis, and customer insight.
- Raise the team’s AI fluency — help analysts reason about model capabilities and limits, interpret evaluation and performance results, and adopt AI tools to work faster and sharper.
- Lead cross-functional agile team members without direct authority — aligning engineering, UX, data science, and business partners around shared outcomes, clear success metrics, and delivery accountability.
- Build a team culture grounded in customer discovery, technical and AI depth, commercial discipline, and ownership of outcomes over activity.
Commercial and Cross-Functional Execution
- Own business-case integrity for the family: define and monitor adoption, attach and take rates, retention, revenue, loss performance, and the unit economics of AI features (e.g., signal and inference cost per scored order versus value returned).
- Partner with sales, marketing, finance, legal, underwriting, and operations on pricing, packaging, positioning, launch readiness, and go-to-market execution.
- Remove blockers, manage dependencies, and keep the team shipping reliably across a complex and evolving product family.
- Ensure product claims, coverage language, and customer-facing materials accurately reflect what the product actually does.
Qualifications
- Experience: 5+ years of product management experience building and scaling software products (SaaS, enterprise platforms, APIs), with a proven track record of shipping complex capabilities to production.
- People Leadership: Experience managing, mentoring, or formally developing analysts or junior product talent, and leading cross-functional agile teams without direct authority.
Product Mindset & High Agency: Exceptional ownership mentality, bias for action, and comfort operating in ambiguous, fast-moving 0* 1 and scaling environments.
- AI Fluency: Practical understanding of modern AI/ML — predictive modeling, scoring and calibration, evaluation and monitoring, guardrails, human-in-the-loop design, and generative/agentic patterns — and how to apply them to real user problems without needing to be an ML engineer.
- Technical Acumen: Comfortable partnering closely with engineering on API design, architectural tradeoffs, scalability, security, and data privacy.
- Domain Expertise (Preferred): Background in e-commerce risk, payments, fraud and chargebacks, identity, insurance, or logistics and post-purchase experiences.
- Commercial Judgment: Able to build and defend a business case, reason about unit economics, and influence pricing and packaging decisions.
- Communication & Influence: Executive-level communication; able to influence without authority across engineering, data science, sales, marketing, and legal.
- Agile Execution: Fluent in dual-track agile, continuous discovery, rapid prototyping, and metric-driven iteration.
- Education: Bachelor’s degree in Computer Science, Business, Engineering, or a related analytical field, or equivalent practical experience.
Pay Range:
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $135,780/year to $251,580/year. Pay is based on several factors including but not limited to, market location and may vary depending on job-related knowledge, skills, and education/training and a candidate’s work experience. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company’s performance. Hired applicants may be eligible for Medical/prescription drug coverage, Dental coverage, Vision coverage, Flexible Spending Account, Health Savings Account, Dependent Care Flexible Spending Account, Basic and Supplemental Life Insurance & Accidental Death and Dismemberment, Disability Income Protection Plan, Employee Assistance Program, 401(k) retirement program, Vacation, Paid Holidays and Personal time, Paid Sick and Family and Medical Leave time as required by law, and Discounted Employee Stock Purchase Program.
Employee Type:
Permanent UPS is committed to providing a workplace free of discrimination, harassment, and retaliation.
Other Criteria:
UPS is an equal opportunity employer. UPS does not discriminate on the basis of race/color/religion/sex/national origin/veteran/disability/age/sexual orientation/gender identity or any other characteristic protected by law.
Basic Qualifications:
Must be a U.S. Citizen or National of the U.S., an alien lawfully admitted for permanent residence, or an alien authorized to work in the U.S. for this employer.
Salary Context
This $135K-$251K range is above the median for AI Product Manager roles in our dataset (median: $193K across 93 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 8,943 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At UPS, 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 Required
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 $220,200 based on 862 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($193K) sits 12% below the category median. Disclosed range: $135K to $251K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
UPS AI Hiring
UPS has 3 open AI roles right now. They're hiring across AI Architect, AI Product Manager, MLOps Engineer. Positions span Alpharetta, GA, US, Mahwah, NJ, US, Chennai, Tamil Nadu, India. Compensation range: $251K - $251K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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