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ROLE SUMMARY
As Vice President, Commercial AI Transformation \& Delivery, you own the vision, strategy, and delivery of artificial intelligence across Pfizer's U.S. commercial organization. You lead a portfolio of AI and data initiatives spanning sales, marketing, and medical, and you are accountable for turning Pfizer's AI ambition into capabilities that are delivered, adopted, and creating value across the business.
Reporting to the Head of Commercial AI, you lead a multi\-disciplinary organization of senior directors, directors, program leaders, and delivery teams, together with a managed network of vendors and delivery partners. While business ownership for individual initiatives sits with their respective business sponsors, you are accountable for end\-to\-end delivery, technical integrity, talent, and the coherence of the overall portfolio. Your scope spans the U.S. commercial organization and the shared platforms, data, and standards these initiatives build on.
Why This Role Matters
Pfizer's purpose — breakthroughs that change patients' lives — has always depended on the company's willingness to transform how it works, not only what it discovers. Across its history, Pfizer has led large\-scale change in how medicines are developed and brought to the patients and clinicians who need them. Becoming the most AI\-forward company, and reinventing how we work from end to end, is the next chapter in that story.
This role is central to that ambition for the commercial organization. It brings strategy, technology, and teams together so that AI is applied with rigor and at scale — improving how Pfizer serves patients, customers, and colleagues, and strengthening Pfizer's leadership in the industry. The work is as much about people and ways of working as it is about technology, and this role leads both.
Candidate Profile
This is a senior business and delivery leadership role. It calls for executive leadership, genuine technical credibility, and subject\-matter depth in AI. Strong candidates bring three capabilities together rather than excelling in one alone:
AI\-native. You have led the development and delivery of production AI — including modern generative and agentic AI — and you reason fluently about how it creates value, where it falls short, and how to deploy it responsibly. You set technical direction and standards through credibility, not by being the hands\-on builder.
Enterprise transformation. You have led significant transformation inside large, complex, regulated organizations — reshaping operating models and ways of working, not only delivering technology. You know how to move an organization, not just a roadmap.
Building from the ground up. You have established new capabilities, products, or teams from the ground up and scaled them to real adoption in enterprise settings — comfortable with ambiguity and able to create momentum without waiting for perfect conditions.
ROLE RESPONSIBILITIES
1\) Vision, Strategy \& Roadmap — Own the vision, strategy, and roadmap for applying AI across the commercial organization. Translate enterprise ambition into a prioritized portfolio of initiatives with clear business value and a credible path to delivery.
2\) End\-to\-End Delivery — Lead delivery of the portfolio from concept through production and adoption. Set priorities, sequence work, resolve dependencies, and make the trade\-off calls on scope, investment, and pace.
3\) Organization \& Talent Leadership — Build and lead a multi\-disciplinary organization of senior directors, directors, program leaders, engineers, data scientists, and product leaders. Attract, develop, and retain top talent, and lead other people\-leaders.
4\) Vendor \& Partner Leadership — Build and direct a network of vendors, contractors, and delivery partners. Define the work, set the standard, manage performance and spend, and keep critical capability and intellectual property within Pfizer.
5\) Platforms, Standards \& Reuse — Ensure initiatives build on shared platforms, data, and engineering standards. Drive reuse and consistency, and prevent duplication and fragmentation across the portfolio.
6\) Executive Leadership \& Influence — Operate as a trusted partner to senior business and functional leaders. Articulate the vision, influence at the most senior levels, and provide consultative guidance, education, and direction across the organization.
7\) Operating Model \& Ways of Working — Lead the shift to AI\-enabled ways of working. Partner with business leaders and PX on capability building, new roles, and leadership development for an AI\-enabled organization.
8\) Governance, Risk \& Responsible AI — Ensure work is delivered responsibly and in line with Pfizer's legal, regulatory, compliance, privacy, and quality requirements. Embed appropriate governance, controls, and accountability into delivery.
9\) Technology \& Industry Awareness — Track relevant technology, vendor, and industry developments, and apply them to prioritize and evolve Pfizer's approach. Distinguish durable, valuable capability from hype.
Basic Qualifications
- 15\+ years of progressive leadership in technology, data and AI, digital, or product delivery, including senior leadership of large, multi\-disciplinary organizations.
- Significant experience in the pharmaceutical, life sciences, or healthcare industry, including an understanding of the commercial environment (sales, marketing, and/or medical) and its regulatory context.
- Proven track record leading complex, enterprise\-scale technology or AI programs from strategy through production and adoption, with measurable business impact.
- Experience leading teams that build and operate production AI/ML and data products — with the credibility to set technical direction and standards without needing to be the hands\-on engineer.
- Demonstrated success leading large\-scale transformation and change in a complex, matrixed enterprise — reshaping operating models and ways of working, not technology alone.
- Track record of establishing new capabilities, products, or businesses from the ground up and scaling them.
- Experience building, leading, and developing senior teams, including other people\-leaders, and attracting top technical and product talent.
- Experience owning significant budgets and managing large vendor and partner portfolios.
- Ability to influence and partner with the most senior executives, and to translate complex technology into clear business terms.
- Bachelors Degree Required.
Preferred Qualifications
- Advanced degree in a technical, quantitative, or business discipline, or equivalent experience strongly preferred.
- Deep subject\-matter expertise in AI/ML and generative and agentic AI, and how they create value in a commercial setting.
- Direct experience in, or close partnership with, pharmaceutical or healthcare commercial functions.
- Familiarity with enterprise data, platform, and AI governance considerations at scale.
- Recognized leadership presence, internally and externally, on applied AI and transformation.
- Experience operating in a global organization across multiple regions and markets.
Last Day to Apply: June 30, 2026
Location: Must be located onsite 2\.5x weekly
The annual base salary for this position ranges from $274,000\.00 to $456,600\.00\. In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 30\.0% of the base salary and eligibility to participate in our share based long term incentive program. We offer comprehensive and generous benefits and programs to help our colleagues lead healthy lives and to support each of life’s moments. Benefits offered include a 401(k) plan with Pfizer Matching Contributions and an additional Pfizer Retirement Savings Contribution, paid vacation, holiday and personal days, paid caregiver/parental and medical leave, and health benefits to include medical, prescription drug, dental and vision coverage. Learn more at Pfizer Candidate Site – U.S. Benefits \| (uscandidates.mypfizerbenefits.com). Pfizer compensation structures and benefit packages are aligned based on the location of hire. The United States salary range provided does not apply to Tampa, FL or any location outside of the United States.
Relocation assistance may be available based on business needs and/or eligibility.
Candidates must be authorized to be employed in the U.S. by any employer.
U.S. work visa sponsorship (such as TN, O\-1, H\-1B, etc.) is not available for this role now or in the future.
Sunshine Act
Pfizer reports payments and other transfers of value to health care providers as required by federal and state transparency laws and implementing regulations. These laws and regulations require Pfizer to provide government agencies with information such as a health care provider’s name, address and the type of payments or other value received, generally for public disclosure. Subject to further legal review and statutory or regulatory clarification, which Pfizer intends to pursue, reimbursement of recruiting expenses for licensed physicians may constitute a reportable transfer of value under the federal transparency law commonly known as the Sunshine Act. Therefore, if you are a licensed physician who incurs recruiting expenses as a result of interviewing with Pfizer that we pay or reimburse, your name, address and the amount of payments made currently will be reported to the government. If you have questions regarding this matter, please do not hesitate to contact your Talent Acquisition representative.
EEO \& Employment Eligibility
Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, disability or veteran status. Pfizer also complies with all applicable national, state and local laws governing nondiscrimination in employment as well as work authorization and employment eligibility verification requirements of the Immigration and Nationality Act and IRCA. Pfizer is an E\-Verify employer. This position requires permanent work authorization in the United States.
Pfizer endeavors to make www.pfizer.com/careers accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process and/or interviewing, please email [email protected]. This is to be used solely for accommodation requests with respect to the accessibility of our website, online application process and/or interviewing. Requests for any other reason will not be returned.
To learn more about acceptable and prohibited uses of AI during the recruitment process, please review our candidate AI\-use guidelines available on Pfizer Careers.
Information \& Business Tech
Salary Context
This $274K-$456K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Pfizer, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. This role's midpoint ($365K) sits 102% above the category median. Disclosed range: $274K to $456K.
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.
Pfizer AI Hiring
Pfizer has 8 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Positions span New York, NY, US, Remote, US, Cambridge, MA, US. Compensation range: $176K - $456K.
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/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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