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About This Role
Company Description About AbbVie
AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on LinkedIn, Facebook, Instagram, X and YouTube.
Job Description
AbbVie is seeking a strategic and execution-oriented Senior Manager, AI Product Management, Finance to own the strategy, performance, governance, and value realization of a major portfolio of AI-enabled products for Finance.
This leader will serve as the accountable product and strategy bridge between business sponsors, AI engineering teams, data platforms, architecture, governance, risk, compliance, and change management functions. The role establishes portfolio direction, translates business needs into executable product strategies and roadmaps, and ensures AI products deliver measurable outcomes across large programs, multiple stakeholder groups, and enterprise priorities.
The ideal candidate combines advanced product management expertise with practical understanding of artificial intelligence, analytics, digital transformation, financial value creation, governance, organizational change, and talent development. Success requires leading through influence, resolving ambiguous enterprise-level problems, balancing innovation with compliance, and building scalable operating mechanisms that advance AbbVie’s long-term business strategy.
Responsibilities:
AI Product Strategy & Vision
- Set the product vision, strategy, standards, and multi-year roadmap for AI-enabled products and capabilities across the Finance Team
- Partner with Finance and BTS leaders to identify high-impact AI opportunities that improve performance, decision quality, risk reduction, and business outcomes.
- Establish portfolio objectives, success metrics, adoption targets, value realization measures, and executive-level performance reporting.
- Develop and govern business cases, investment priorities, funding tradeoffs, and resource allocation recommendations based on strategic alignment, financial impact, risk reduction, compliance, and operational benefit.
Product Ownership & Delivery
- Oversee the full product lifecycle across major programs and products, from opportunity framing through deployment, scaling, sustainment, and benefit realization.
- Translate business challenges into prioritized product backlogs and clear user requirements.
- Guide Product Owners, Product Managers, Agile delivery teams, and cross-functional partners to ensure roadmaps, backlogs, releases, and adoption plans remain aligned with enterprise priorities.
- Drive roadmap execution across AI Engineering, Data Engineering, Architecture, UX, and business teams.
- Manage portfolio scope, priorities, dependencies, risks, budget implications, and tradeoffs across multiple teams and business areas.
Business Partnership & Stakeholder Management
- Build trusted relationships with leaders and key stakeholders across AbbVie.
- Facilitate alignment on priorities, outcomes, funding, and adoption strategies.
- Communicate product vision, roadmap progress, value realization, tradeoffs, risks, and decision needs clearly and persuasively to senior and executive leadership.
- Lead prioritization forums, governance bodies, steering discussions, and cross-functional decision forums that align stakeholders around high-visibility AI investments.
AI Adoption & Value Realization
- Measure product success through adoption, engagement, business outcomes, productivity gains, and financial impact.
- Partner with change management and training teams to accelerate adoption.
- Ensure products solve real user problems and drive sustainable behavior change.
- Track KPIs and continuously refine products using user insights and operational metrics.
Responsible AI & Governance
- Own AI product governance expectations for the portfolio, ensuring alignment with AbbVie policies, compliance requirements, privacy standards, quality expectations, data security, risk controls, and Responsible AI principles.
- Partner with Legal, Compliance, Security, Quality, Privacy, Architecture, and Risk teams to establish controls, mitigate risks, support audit readiness, and respond to issues throughout the product lifecycle.
- Sponsor transparency, explainability, human oversight, model risk mitigation, documentation rigor, and scalable governance practices for AI-enabled solutions.
Product Management Excellence
- Establish and scale AI product management with best practices, frameworks, and tooling.
- Develop Product Managers, Product Analysts, business product owners, and emerging leaders through coaching, mentoring, capability building, and clear product management standards.
- Contribute to the development of a product-centric operating model for Data & AI
- Promote experimentation, customer-centricity, and evidence-based decision making.
Qualifications Required:
- Bachelor's Degree in Business, Computer Science, Engineering, Information Systems, Data Science, or related fields plus 8 years’ experience; OR Master’s Degree plus 7 years’ experience; OR PhD plus 3 years’ experience
- Respective years of experience in product management, technology strategy, digital transformation, analytics, program leadership, or related disciplines, with demonstrated ownership of large programs, portfolios, or critical business capabilities.
- 3+ years leading AI, machine learning, analytics, automation, or digital products
- Enterprise AI product strategy: Ability to define a compelling product vision, multi-year roadmap, portfolio standards, investment priorities, and measurable outcomes aligned with business strategy.
- End-to-end product and portfolio leadership: Proven ability to lead complex AI products from opportunity framing through delivery, scaling, sustainment, adoption, and benefit realization across multiple teams and dependencies.
- Business and executive partnership: Strong business acumen and influence skills to align senior leaders on priorities, funding, tradeoffs, risks, decisions, and adoption without relying on direct authority.
- AI, data, and delivery fluency: Practical understanding of AI, machine learning, analytics, data platforms, architecture, user experience, and Agile delivery sufficient to translate business needs into executable product strategies, roadmaps, and backlogs.
- Value realization and adoption: Ability to build business cases, establish KPIs, quantify financial and operational impact, use customer insights, and drive sustained adoption and behavior change.
- Responsible AI and governance: Demonstrated capability to embed privacy, security, quality, compliance, model risk, human oversight, documentation, and audit-readiness throughout the AI product lifecycle.
- Operating model and talent development: Experience establishing scalable product management practices and coaching Product Managers, Product Owners, analysts, and cross-functional teams to improve product discipline and delivery performance.
- Experience setting strategy, managing portfolio-level priorities, influencing cross-functional leaders, and communicating recommendations, risks, and tradeoffs to senior leadership.
- Demonstrated experience managing product roadmaps and Agile delivery teams.
- Strong executive communication and stakeholder management skills
- Proven ability to influence across organizational boundaries without direct authority.
- Demonstrated ability to manage compliance, risk, governance, audit-readiness, and documentation expectations for technology-enabled products or programs.
- Experience building business cases and measuring value realization.
Preferred:
- Experience in healthcare, pharmaceuticals, life sciences, manufacturing, or supply chain environments with a focus on Finance
- Familiarity with Generative AI, machine learning, data platforms, LLMs, AI governance, and MLOps concepts
- Experience with Jira, Azure DevOps, Productboard, or similar product management tools
- MBA or advanced technical degree
Additional Information
Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law:
- The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. This range may be modified in the future.
- We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees.
- This job is eligible to participate in our long-term incentive programs.
Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company’s sole and absolute discretion, consistent with applicable law.
AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled.
US & Puerto Rico only - to learn more, visit https://www.abbvie.com/join-us/equal-employment-opportunity-employer.html
US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more:
https://www.abbvie.com/join-us/reasonable-accommodations.html
Salary Context
This $124K-$236K range is below the median for AI Product Manager roles in our dataset (median: $194K across 123 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 9,079 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At AbbVie, 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 947 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $124K to $236K.
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.
AbbVie AI Hiring
AbbVie has 8 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, AI Product Manager, Data Engineer. Positions span Irvine, CA, US, Mettawa, IL, US, North Chicago, IL, US. Compensation range: $236K - $346K.
Location Context
AI roles in Chicago pay a median of $193,440 across 429 tracked positions. That's 9% below 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 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).
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 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
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