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Location Boston, Massachusetts, United States Job ID R\-254347 Date posted 10/06/2026
About the Role
AstraZeneca is on a bold journey to become the most data\-driven biopharmaceutical company in the world. Central to this is our Operational Data Strategy (ODS) function \- a team reshaping how data powers everything to inform enterprise decision\-making.
As Senior Director, AI Data Applications \& Transformation, you will own the business case for AI across ODS \- identifying where AI creates the greatest value for operational and patient data, defining the outcomes we need to achieve, and partnering with Data Products \& Platforms to bring those solutions to life. You are the bridge between business ambition and AI capability: equal parts strategist, problem owner, and transformation leader.
You will lead a team of AI application specialists and transformation professionals embedded in ODS. Reporting to the Executive Director of Operational Data Strategy, you will be based at our Boston hub with strong connections to global stakeholders across R\&D.
What You'll Do
AI Application Strategy \& Demand Definition
- Own the AI use case pipeline: partner with business leaders across AstraZeneca to identify, prioritise, and define where AI applications will create the greatest operational value, with data as the backbone.
- Translate business problems into well\-scoped AI application briefs \- articulating the desired outcome, data requirements, and success criteria \- for delivery in close partnership with Data Products \& Platforms.
- Define the 'what' and 'why' of AI applications; Data Products \& Platforms owns the 'how' and the build. Hold that boundary clearly and collaborate across it effectively.
- Maintain a forward\-looking view of how AI capabilities can evolve AstraZeneca's operational model and translate that into a prioritised roadmap aligned with enterprise strategy.
AI Portfolio Ownership \& Performance
- Own the in\-life AI application portfolio \- its performance and the decisions to scale, sustain, or retire solutions.
- Drive activation into the business \- turning AI solutions into real changes in how teams work, not just deployments.
- Stay accountable for the portfolio’s realised impact \- the operational outcomes and adoption it delivers, not only the demand it defines.
Enterprise AI Transformation
- Partner with senior business leaders to identify high\-value transformation opportunities where AI can create step\-change improvements in operational performance.
- Drive adoption of AI capabilities by working with change, communications, and training functions to embed new ways of working sustainably across the business.
- Champion an AI\-embedded model of operating \- ensuring AI is a natural part of how every team in ODS works, not a separate function sitting apart from the business.
Leadership \& Stakeholder Engagement
- Lead, develop, and inspire a team of AI application specialists and transformation professionals, fostering a culture of curiosity, delivery, and continuous learning.
- Build strong partnerships with R\&D and Technology to align AI application priorities with enterprise strategy.
- Represent ODS at senior leadership forums and communicate AI application value clearly to executive audiences.
- Collaborate with external partners, vendors, and academic institutions \- including collaborations such as Tufts and TransCelerate \- to accelerate delivery and bring world\-class AI capability into the business.
Responsible AI \& Impact Measurement
- Ensure all AI applications are deployed in line with AstraZeneca's ethical AI principles, privacy requirements, and regulatory obligations \- partnering with Data Products \& Platforms \& Data Governance, Risk and Trust on the underlying governance infrastructure.
- Own the value narrative for AI applications \- articulating the business outcomes each application must deliver and partnering with the dedicated metrics, adoption and literacy team to ensure impact is measured and demonstrated to senior leadership.
Key Deliverables
In your first 12 months
- A prioritised, AI use\-case pipeline and roadmap for ODS, agreed with business leaders and aligned to enterprise strategy.
- One to two prioritised AI\-led transformation programmes, scoped with business owners, with target step\-change operational outcomes defined and endorsed by senior leadership.
- A clearly articulated value case for the AI application portfolio \- the business outcomes each priority application must deliver \- agreed with business owners and measured in partnership with the metrics, adoption and literacy team.
- A responsible\-AI operating standard for AI applications, agreed with Data Governance, Risk and Trust and embedded across the portfolio.
- A team and capability plan for the AI application and transformation group, with priority hires in place.
On an ongoing basis
- A quarterly\-refreshed AI application portfolio and prioritised roadmap aligned to enterprise strategy.
- Sustained executive alignment and sponsorship for the AI agenda across R\&D and Technology, supported by regular value and adoption readouts to senior leadership forums.
- AI\-enabled ways of working embedded across ODS teams, with usage and business outcomes evidenced in partnership with the adoption and literacy team.
What We're Looking For
Essential
- 10\+ years of experience in data and technology roles, with at least 5 years in senior leadership positions delivering AI or advanced analytics products at scale.
- Bachelor’s degree
- Proven track record of defining AI use cases, owning business outcomes, and working with platform or engineering partners to deliver in a complex enterprise environment.
- Strong fluency across the modern AI landscape \- LLMs, agentic AI, and ML models with enough technical depth to be a credible partner to teams.
- Experience leading cross\-functional transformation programmes with measurable business impact.
- Exceptional communication and influencing skills; able to translate complex AI concepts for senior non\-technical audiences and build conviction around a roadmap.
- Demonstrated ability to operate at both strategic and hands\-on levels \- setting direction and rolling up sleeves when needed.
- Experience managing and developing high\-performing, multi\-disciplinary teams.
Desirable
- Experience in the pharmaceutical, life sciences, or broader healthcare industry.
- Familiarity with responsible AI frameworks and enterprise AI governance.
- Background in working at the interface of business and technology \- product management, business architecture, or similar.
- Experience operating in a global, matrixed organisation.
The annual base pay (or hourly rate of compensation) for this position ranges from 210,336\.00 \- 315,504\.00 USD Annual (80% \- 120%). Our positions offer eligibility for various incentives—an opportunity to receive short\-term incentive bonuses, equity\-based awards for salaried roles and commissions for sales roles. Benefits offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans.
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.
Why AstraZeneca:
Here, AI and data are embedded in the way we discover, develop, and deliver medicines, giving you the scale, trust, and access to make change that matters. You will work at the intersection of cutting\-edge AI and world\-class science, shaping applications that speed decisions from the bench to the clinic and ultimately to patients worldwide. We bring unexpected teams together—engineers, data scientists, clinicians, and operators—to solve complex problems with urgency and care. In Boston, you will be connected to vibrant biotech and AI ecosystems while collaborating globally across functions that value kindness alongside ambition. Your leadership will help translate science into outcomes, backed by meaningful sponsorship, modern platforms, and a culture that prizes clarity, learning, and impact.
Call to Action:
Lead the AI applications agenda that accelerates life\-changing medicines—take the next step and shape how a global enterprise runs on data and AI!
*As AstraZeneca continues to put patients at the forefront of our mission, we are excited for our move to Kendall Square/Cambridge in 2026\. Find out more information here*: Kendall Square Press Release
Date Posted
11\-Jun\-2026
Closing Date
25\-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 $210K-$315K 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 ($262K) sits 45% above the category median. Disclosed range: $210K to $315K.
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
AI roles in Boston pay a median of $215,350 across 442 tracked positions. That's 8% 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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