Job Description:

Role Overview

The AI Product Director is a senior leadership role responsible for defining, building, and scaling UPS Digital's portfolio of AI-driven logistics solutions and the platform that powers them - a connected suite of supply chain visibility, integration, and agentic capabilities that help customers simplify complex supply chain decisions, optimize their logistics, integrate more easily with UPS, and unlock greater value from UPS data, services, and digital platforms.

This leader will own a portfolio at the intersection of applied AI, logistics technology, platform products, SaaS, APIs, and customer-facing digital experiences. The role requires strong technical acumen and the ability to work deeply with engineering, data science, machine learning, architecture, UX, and business partners to turn complex logistics, integration, and customer workflow problems into scalable, intelligent product experiences.

The Product Director will be expected to bring strong Product Operating Model experience, including clear product accountability, outcome-based roadmaps, empowered cross-functional teams, continuous discovery, disciplined prioritization, experimentation, and rapid iteration. This leader should be equally comfortable operating in 01 new product development and in scaling established platforms - moving from customer problem validation and MVP definition to product-market fit testing, commercialization, adoption, and measurable business impact.

A core part of the role is identifying where AI - including predictive insight, automation, decision support, and generative and agentic capabilities - can create differentiated customer value. This includes evolving the portfolio's supply chain visibility products beyond dashboards into intelligent recommendations, autonomous exception handling, and agent-assisted decisioning; expanding API and integration capabilities so customers and partners can connect to UPS with less friction; and incubating new AI-native solutions that reduce complexity and improve speed, confidence, and control across the shipping and supply chain experience.

The ideal candidate is a hands-on product leader who has shipped AI/ML or generative-AI products in production and understands what it takes to make them reliable, safe, and valuable - including grounding models in proprietary data, designing human-in-the-loop workflows, building evaluation and monitoring practices, and managing the realities of model performance, cost, latency, and customer trust. They should have a track record of launching products in ambiguous environments, designing experiments to test customer demand, using evidence to make investment decisions, and iterating quickly based on customer feedback, usage data, technical feasibility, and commercial potential.

This is also a people leadership role. The Director will manage and develop Product Managers, lead cross-functional product pods, and create a culture grounded in customer discovery, experimentation, technical and AI depth, commercial discipline, and accountability for outcomes.

Key Responsibilities

Portfolio Strategy and Product Leadership

• Define and lead the product strategy for the Cognitive Logistics portfolio, spanning supply chain visibility, integration and API products, and new AI-enabled digital solutions.
• Establish a clear portfolio vision that connects customer needs, UPS strategic priorities, market opportunity, technical feasibility, and measurable business outcomes.
• Balance investment across established platforms, growth-stage products, and 01 new solution development.
• Translate customer insights, market signals, product performance, and internal business needs into a prioritized, outcome-based roadmap.
• Represent the portfolio in executive discussions, customer conversations, roadmap reviews, and strategic planning forums.

AI, Data, and Intelligent Product Development

• Set the portfolio's applied-AI strategy - identifying where predictive analytics, automation, recommendations, generative AI, and agentic (autonomous, tool-using) capabilities create the most customer and business value.
• Partner with data science, ML, engineering, architecture, and UX teams to define AI-enabled products that solve real customer workflow, visibility, and decisioning problems.
• Evolve visibility and reporting products into intelligent solutions that anticipate risk, recommend actions, orchestrate exception handling, and - where appropriate - act autonomously with the right human-in-the-loop controls.
• Champion a proprietary-data advantage: work with data and engineering teams to turn UPS's unique logistics data into durable model and product differentiation (e.g., grounding, retrieval, and fine-tuning strategies).
• Establish the disciplines that make AI products trustworthy in production - evaluation frameworks, offline and online testing, performance and drift monitoring, guardrails, and clear ownership of model quality and outcomes.
• Embed responsible-AI practices into the product lifecycle, including fairness, transparency, data privacy and sovereignty, security, and appropriate governance and risk controls.
• Make pragmatic build-vs.-buy and model-selection decisions, favoring a model-agnostic architecture that manages cost, latency, accuracy, and vendor risk.
• Stay current on the fast-moving AI landscape and translate emerging capabilities into concrete, feasible opportunities for UPS customers.

New Product Development and Experimentation

• Lead 01 product development for new solutions, from problem discovery through MVP definition, experimentation, pilot design, and product-market fit validation.
• Develop and test hypotheses around customer pain points, adoption potential, commercial value, technical feasibility, and operational scalability.
• Use customer interviews, prototypes, pilots, usage data, model evaluations, and market feedback to decide whether to scale, pivot, pause, or sunset new concepts.
• Leverage AI-native ways of working - rapid prototyping, synthetic data, and AI-accelerated discovery and analysis - to shorten learning cycles and reduce time to insight.
• Partner with sales, marketing, finance, operations, and customer-facing teams to validate positioning, target segments, pricing, packaging, and go-to-market strategy.
• Build disciplined experimentation and iteration practices that reduce uncertainty before larger product, technology, or commercial investments are made.

Platform, SaaS, API, and Technical Product Ownership

• Lead product strategy for platform and integration-oriented capabilities, including UPS's APIs, developer tooling, and partner integration products.
• Improve the customer, developer, and partner experience by simplifying onboarding, integration, discoverability, documentation, reliability, usability, and adoption.
• Partner closely with engineering and architecture teams to define scalable product capabilities, understand technical tradeoffs, and make informed roadmap decisions.
• Bring strong technical acumen to discussions involving APIs, data products, platform extensibility, system integrations, AI-enabled services, and enterprise SaaS - including modern patterns such as tool/function calling and interoperability standards for agents.
• Identify opportunities to turn UPS capabilities, data, and services into more modular, reusable, and scalable digital products.

Product Operating Model and Cross-Functional Execution

• Apply modern Product Operating Model practices, including clear product ownership, empowered cross-functional teams, outcome-based roadmaps, continuous discovery, and faster learning loops.
• Lead product pods across engineering, UX/design, data, research, architecture, and business partners with clear priorities, decision rights, success metrics, and delivery accountability.
• Establish operating rhythms for discovery, roadmap tradeoffs, experimentation, launch readiness, post-launch measurement, and continuous improvement.
• Shift teams from activity-based execution to outcome-based product management focused on customer value, adoption, revenue, retention, and business impact.
• Remove blockers, manage dependencies, and ensure teams maintain momentum across a complex and evolving portfolio.

Commercial and Business Ownership

• Own business-case integrity and performance outcomes across the portfolio.
• Define and monitor success metrics across adoption, usage, retention, revenue, customer satisfaction, operational impact, and strategic value - including the unit economics of AI features (e.g., inference cost per outcome).
• Partner with sales, marketing, finance, strategy, and operations to shape commercialization plans for new and existing products.
• Influence pricing, packaging, customer targeting, channel strategy, and launch planning where relevant.
• Use product, customer, financial, and operational data to assess performance and guide future investment decisions.

People Leadership and Product Culture

• Manage, coach, and develop Product Managers responsible for the portfolio's products and capabilities.
• Build a high-performing product culture grounded in customer discovery, technical and AI depth, experimentation, commercial discipline, and accountability for outcomes.
• Raise the team's AI fluency - helping PMs reason about model capabilities and limits, evaluation, and responsible use - and adopt AI tools to work faster and smarter.
• Coach Product Managers on product strategy, prioritization, stakeholder influence, technical partnership, executive communication, and business ownership.
• Create clarity on roles, decision rights, ownership, escalation paths, and portfolio-level tradeoffs.
• Develop future product leaders who can operate effectively across strategy, execution, customer insight, technical partnership, and measurable business impact.

Qualifications

• Bachelor's degree in Business, Computer Science, Engineering, or a related field; MBA or advanced degree preferred.
• 8+ years of product management experience, with at least 3 years leading product at the director level or equivalent.
• Proven track record of owning and scaling SaaS or technology products, with direct accountability for revenue targets ($10M+).
• Hands-on experience with new product development - including PMF testing, customer discovery, and zero-to-one product builds.
• Demonstrated experience shipping AI/ML products to production, and working familiarity with generative and agentic AI - comfortable partnering with data science and engineering to define, build, evaluate, and launch AI-enabled capabilities.
• Practical understanding of the modern AI stack - e.g., LLMs, retrieval-augmented generation (RAG), evaluations and observability, guardrails, and human-in-the-loop design - without needing to be an ML engineer.
• Commitment to responsible AI, including data privacy, security, governance, and model-risk considerations.
• Strong cross-functional leadership experience, including leading engineering and design pods in an agile environment.
• Executive-level communication skills; able to influence without authority and represent the product as a strategic business driver.
• Familiarity with modern product frameworks (dual-track agile, continuous discovery, jobs-to-be-done, etc.).

Additional Information:

• Must be located in the Greater Atlanta geographical area or willing to self- relocate
• No sponsorship available for this role

Employee Type:
Permanent