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Location Gaithersburg, Maryland, United States Job ID R\-253793 Date posted 04/06/2026
Role: Senior Director, AI Commercial Business Partner \- Oncology Business Unit US
Location: Gaithersburg US
Salary: Competitive \+ Excellent Benefits
Do you have an organizational mindset and \-can\-do attitude that enables you to deliver positive outcomes on high impact and complex AI projects? Are you looking to join a growing team that will design and deliver value\-driven change to an organization, do you relish the opportunity to drive wide scale commercial innovation across teams and lead the AI revolution in commercial for AstraZeneca.
About AstraZeneca:
At AstraZeneca, we are proud of our ground\-breaking pipeline and continue to look for new ways to bring the best medicines to patients. We are building and fostering high\-performing collaborative teams across the organization to help us deliver on our ambition. There’s no better time to join our global, growing enterprise as we lead the way for patients, healthcare and broader society.
AI Commercial Team (AIC):
The AI Commercial team, is part of the newly founded Enterprise AI team (EAI), delivering AI strategy and coordinated commercial progress and programs, in close collaboration with our business teams, and in partnership with our enterprise teams. We lead cross\-functional and commercial\-wide initiatives to deliver transformative commercial value and ensure we are at our best in delivering lifesaving medicines to patients.
What you’ll do:
AI Commercial Business Leaders are the embedded “translators” between business functions and the AI Commercial organization, responsible for the success of AI programs. The role is strategically positioned in therapy areas, markets, and functions with the objectives to drive translation, adoption, and lifecycle management of AI solutions in the business.
Key deliverables include translating business needs into well\-scoped AI use cases and ensure each solution delivers measurable business impact. This requires you to be bilingual in business/science and technology, enabling you to connect domain priorities with AI delivery teams. You will be responsible for owning the full lifecycle of AI solutions \- from ideation through delivery, adoption, and decommissioning \- and are accountable for driving real, sustained value and reuse.
In this role, you will be responsible for building the bridge between business teams at a Region and Market level, and technical experts within the AIC and EAI function as well as key other Enterprise technology teams such as Commercial IT and Global Business Services. As part of this role, you will champion the key commercial AI programs in service of enhancing customer engagement, powered by actionable AI insights, delivered thought agentic platforms. In addition, you will partner on the delivery of AI capability building programs.
To do this successfully, you will share geographic location with the respective Region team, have a seat at the Senior leader table for the Region, with connection further enhanced through dotted line reporting to the Region, while sitting on the AIC Leadership team reporting to VP AIC.
Oncology Business Unit (OBU) is responsible for the commercialisation of medicines across some of the most hard\-to\-treat forms of disease, including: lung, breast, gastrointestinal, gynaecological, genitourinary and haematology cancers.
Responsibilities include:
- Strategy and portfolio ownership of new AI Commercial programs for in partnership with the business teams.
- Single point of contact and responsible for engagement for AI Commercial capabilities programs for the Region, maintaining AI roadmaps aligned with business needs, developing business cases in partnership with the business, and promoting reuse principles.
- Translation of needs into AI use cases: Engaging with stakeholders to identify unmet needs and translating these into well\-defined AI use cases with clear metrics and requirements and a clear alignment with business strategy and AI30 focus areas.
- Delivery, scale, adoption and value tracking of AI initiatives: co\-leading solution delivery, overseeing lifecycle management including pilot shaping, deployment, adoption, and value realization, while ensuring appropriate evidence frameworks are applied.
- Change management and governance: Acting as change leaders embedding AI into operations, support AI literacy, and ensure data governance and compliance with standards and regulations, while collaborating with internal and external partners to drive commercial success at scale.
- Translating business needs into well\-scoped AI use cases and ensuring each solution delivers measurable business impact. This requires you to be bilingual in business and technology, enabling you to connect domain priorities with AI delivery teams. Owning the full lifecycle of AI solutions \- from ideation through delivery, adoption, and decommissioning.
- Spot and shape new enterprise initiatives by scanning internal/external trends and partnering with functional leaders.
- Track industry, market and innovation shifts to anticipate opportunities and risks.
- Lead change through structured stakeholder engagement, aligning perspectives and guiding teams through uncertainty.
- Define KPIs and monitoring to evaluate progress and pivot when needed.
Typical Accountabilities include:
- AI Strategy and portfolio ownership
+ Serve as the single business owner for AI solutions in their therapy area, market, or function.
+ Maintain and sequence a clear AI roadmap aligned to business, medical, access, and R\&D priorities.
+ Develop business cases and value narratives to support prioritization decisions.
+ Embed reuse principles ("build or buy once, scale to many") when defining and shaping demand.
- Translation of needs into AI use cases
+ Engage business leaders and cross\-functional teams to surface unmet needs and decision points where AI can drive new outcomes.
+ Translate needs into well\-scoped AI use cases with clear problem statements, success metrics, data requirements, and risk tiering.
+ Act as bilingual translators connecting business priorities, scientific context, and technology capabilities.
- Delivery, scale, adoption and value tracking
+ Co\-lead delivery with enterprise AI teams to ensure solutions fit real workflows and local systems.
+ Own the full AI Program lifecycle, including:
+ Pilot shaping
+ Scaled deployment
+ Local adoption and change readiness
+ Tracking of adoption KPIs
+ Outcome and ROI realization
+ Remediation or decommissioning when needed
+ Ensure correct evidence frameworks are applied to measure value.
- Change management and ways of working
+ Act as change leaders for AI, embedding new practices into day\-to\-day operations – avoiding one\-off pilots that don’t inform or lead to a scaled solution
+ Identify required process and role shifts and work with functional leaders to implement them, in order to advance adoption of AI solutions.
+ Support AI literacy and upskilling by partnering with the AITO team and HR on targeted learnings.
- Data, governance and compliance point of contact for the Oncology Business Unit (OBU) US region
+ Partner with Enterprise Demand Excellence (EDE) and AITT to ensure data used in solutions is AI\-ready and compliant with enterprise standards (e.g., GDPR, HIPAA, AI act, SAMd).
+ Ensure each AI use case follows the enterprise governance framework, including:
+ Registry\-before\-scale
+ Model and data cards
+ Ongoing monitoring
+ Human\-in\-the\-loop oversight when required
+ Ensure AI solutions align with quality, legal, compliance, PV and safety requirements, and local codes.
- Deliver Partnership and ecosystem
- Clear co\-development of AI programs and delivery plans, ensuring business maintains sponsorship and ownership from inception to delivery and scaling.
- Develop clear and deep delivery relationships with Commercial AI and Global Business Services teams. Drive focus across business and enterprise teams to establish clear and mutually aligned remits for coordinated delivery of AI programs
- Build a culture of coordinated delivery with pride, shared purpose and celebration. Supporting a culture of co\-delivery and ability to raise and address concerns in a psychologically safe environment.
- Integration with enterprise AI organization
- Act as the primary interface between your business area and CAI Capability teams (Customer and Patient Engagement, Commercial DS\&AI) and Enterprise AI teams (i.e., AISI, AITC, AICA, AIEI, AITT, EDE), ensuring clarity on ownership, priorities and hand\-offs.
Essential for the Role:
- Minimum 10 years professional pharma experience, with 5\-7 years focus on leading transformation programs in commercial pharma
- Demonstrated commercial and scientific domain experience reflecting a deep understanding of the needs of the business function and Region you are responsible for.
- Strong analytical and communication skills at all levels of the organization and the ability to operate successfully in highly matrixed organizations
- Effective interpersonal and relationship management skills with consultative approach and ability to interact and build trust
- Operational effectiveness, highly organized process with follow through
- Strong initiative to understand our core business goals and establish processes or programs to set direction, generate alignment and push forward
- Financial background and understanding of forecasts, budgets, financial activities and tools
- Resourceful, motivated, influential and proactive; able to prioritize in a fast\-growing organization
- Experience in strategic planning processes and tools and/or project management tools
Desirable for the Role
- Graduate degree (MBA, Masters, MD or PhD preferred)
- Experience in leading and managing alliances or strategic partnerships
- Experience in translating business needs into a coordinated business proposal and plan for cross\-functional senior leadership team
- Ability to develop creative and simple solutions to complex problems
Next Steps?
Are you ready to step up and take ownership of your work, solutions for the business and your personal career development? and join us in our mission to bring life\-changing medicines to people!
Where can I find out more?
Our Social Media,
Follow AstraZeneca on LinkedIn https://www.linkedin.com/company/1603/
Follow AstraZeneca on Facebook https://www.facebook.com/astrazenecacareers/
Follow AstraZeneca on Instagram https://www.instagram.com/astrazeneca\_careers/?hl\=en
*When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life\-changing medicines. In\-person working gives us the platform we need to connect, work at pace and challenge perceptions. That’s why we work, on average, a minimum of three days per week from the office. But that doesn't mean we’re not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.*
*The annual base pay for this position ranges from $203,214 \- $304,820 USD. Base pay offered may vary depending on multiple individualized factors, including market location, job\-related knowledge, skills, and experience.*
*In addition, our positions offer a short\-term incentive bonus opportunity; eligibility to participate in our equity\-based long\-term incentive program. Benefits offered included a qualified retirement program \[401(k) plan]; paid vacation and holidays; paid leaves; and, health benefits including medical, prescription drug, dental, and vision coverage in accordance with the terms and conditions of the applicable plans.*
*Additional details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employee will be in an “at\-will position” and the Company reserves the right to modify base pay (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.*
\#LI\-HYBRID
\#EAI
Date Posted
05\-Jun\-2026
Closing Date
19\-Jun\-2026
Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form.
Salary Context
This $203K-$304K 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 AstraZeneca, 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. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($254K) sits 40% above the category median. Disclosed range: $203K to $304K.
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.
AstraZeneca AI Hiring
AstraZeneca has 8 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boston, MA, US, Wilmington, DE, US, Gaithersburg, MD, US. Compensation range: $204K - $319K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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