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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
The Head of AI Strategic Programs, Commercial BTS will lead strategic AI initiatives to transform AbbVie’s
commercial operations. Integrate advanced AI/ML solutions into commercial functions, optimizing healthcare provider and patient engagement, market strategy, product launch, and sales operations through actionable data insights.
Responsibilities:
- Strategy & Influence: Develop a 3–5 year AI-driven vision for commercial excellence, collaborating with Sales, Marketing, and Commercial Operations leadership.
- Process Redesign & Optimization: Analyze and modernize commercial workflows, using AI for targeted outreach, segmentation, channel optimization, and marketing personalization.
- Solution Consultation & Advisement: Serve as the primary AI advisor for commercial leaders, bridging technical and business perspectives and ensuring solution strategies are aligned with commercial goals.
- Delivery Execution: Oversee delivery of high-impact AI programs, from design to deployment and adoption, using a disciplined program methodology.
- People Management: Build and develop a high-performing team with hybrid analytical and commercial capabilities. Mentor future leaders who can drive commercial-AI transformation.
- Governance & Responsible AI: Enforce ethical, compliance, and privacy standards in commercial AI deployments, ensuring regulatory adherence and fairness in model-driven decisions.
- Resource Management: Accountable for commercial AI program budgets and organizational design.
- Liaison & Relationship Building: Cultivate strong partnerships with Sales, Marketing, IT, Data Science, and external partners; provide advisory support at executive and cross-domain levels.
- Culture: Foster an innovative and customer-centric mindset; demonstrate AbbVie’s leadership principles and support talent growth in AI-powered commercial functions.
Qualifications Required Qualifications:
- Bachelor’s degree required; Master’s in quantitative or business field preferred (e.g., Data Science, Business Analytics, Marketing, MBA).
- 2+ years’ experience in commercial operations, analytics, AI/ML, consulting, or business transformation, with at least 5 years combining technical and commercial leadership.
- Proven success delivering enterprise-scale commercial technology or analytics programs.
- Deep understanding of commercial processes, CRM, sales operations, and digital marketing platforms.
- Strong track record in building and managing analytic/technical teams (4+ professionals).
- Experienced in collaborating with and influencing senior commercial executives.
- Hands-on with modern AI/ML tools relevant to commercial domains.
- Budgetary experience; $20 million+ programs strongly preferred.
In terms of the performance and personal competencies required for the position, teh following is expected:
Setting Strategy
- The ability to create and articulate an inspiring vision for the organization, not only for the areas they are directly responsible for, but the enterprise as a whole.
- The inclination to seek and analyze data from a variety of sources to support decisions and to align others with the organization's overall strategy.
- An entrepreneurial and creative approach to developing new, innovative ideas that will stretch the organization and push the boundaries within the industry.
- The ability to effectively balance the desire/need for broad change with an understanding of how much change the organization is capable of handling, to create realistic goals and implementation plans that are achievable and successful.
Executing for Results
- The ability to set clear and challenging goals while committing the organization to improved performance; tenacious and accountable in driving results.
- Comfortable with ambiguity and uncertainty; the ability to adapt nimbly and lead others through complex situations.
- A risk-taker who seeks data and input from others to foresee possible threats or unintended circumstances from decisions; someone who takes smart risks.
- A leader who is viewed by others as having a high degree of integrity and forethought in their approach to making decisions; the ability to act in a transparent and consistent manner while always taking into account what is best for the organization.
Leading Teams
- The ability to attract and recruit top talent, motivate the team, delegate effectively, celebrate diversity within the team, and manage performance; widely viewed as a strong developer of others.
- The ability to persevere in the face of challenges, and exhibit a steadfast resolve and relentless commitment to higher standards, which commands respect from followers.
- A leader who is self-reflective and aware of their own limitations; leads by example and drives the organization's performance with an attitude of continuous improvement by being open to feedback and selfimprovement.
Relationships and Influence
- Naturally connects and builds strong relationships with others, demonstrating strong emotional intelligence and an ability to communicate clearly and persuasively.
- An ability to inspire trust and followership in others through compelling influence, powerful charisma, passion in their beliefs, and active drive.
- Encourages others to share the spotlight and visibly celebrates and supports the success of the team.
- Creates a sense of purpose/meaning for the team that generates followership beyond their own personality and engages others to the greater purpose for the organization as a whole.
Preferred Qualifications:
- Pharma/Biotech commercial operations experience.
- Background in commercial analytics, multichannel engagement, marketing science, or digital transformation.
- Exposure to regulatory, legal, and compliance considerations in commercial AI/Data Science.
- Experience with enterprise-grade AI/analytics platforms.
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 thisposting 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 anduntil 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 $206K-$393K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At AbbVie, 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 Required
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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($299K) sits 41% above the category median. Disclosed range: $206K to $393K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
AbbVie AI Hiring
AbbVie has 7 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Architect. Positions span Mettawa, IL, US, Worcester, MA, US, Mettawa, IL. Compensation range: $236K - $393K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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).
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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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