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About This Role
Location Boston, Massachusetts, United States Job ID R\-253581 Date posted 08/06/2026
Job Title: Head of Clinical Data ScienceLocation: Boston
At AstraZeneca, we pride ourselves on crafting a collaborative culture that champions knowledge\-sharing, ambitious thinking and innovation – ultimately providing employees with the opportunity to work across teams, functions and even the globe.
Recognizing the importance of individualized flexibility, our ways of working allow employees to balance personal and work commitments while ensuring we continue to create a strong culture of collaboration and teamwork by engaging face\-to\-face in our offices 3 days a week. Our head office is purposely designed with collaboration in mind, providing space where teams can come together to strategize, brainstorm and connect on key projects.
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
Introduction to Role:The Sr Director/Head of Clinical Data Science provides strategic and operational leadership for data science, bioinformatics, and AI/ML functions within the Rare Disease Unit. It drives innovation in clinical development by integrating multi\-omics, imaging, and digital health data into unified analytical platforms, establishing robust AI and data governance, and aligning data science strategy with organisational R\&D priorities. It owns the future direction of mechanism\-centered disease modelling and translational AI within Alexion and sets the vision for the application of innovative statistical and bioinformatics methods.
Accountabilities:
- Providing strategic leadership for clinical data science, bioinformatics, and AI/ML initiatives across the Rare Disease portfolio, ensuring alignment with R\&D objectives.
- Leading the development of Clinical Data Science strategy within Alexion, contributing to the enterprise\-wide data science and AI strategy, and contributing to the Alexion Quantitative Sciences strategy.
- Building and scaling a high\-performing cross\-functional team spanning AI, bioinformatics, and translational medicine, accelerating study design and data\-driven decision\-making.
- Establishing and coordinating AI and data governance frameworks aligned with regulatory standards (FDA, 21 CFR Part 11, CDISC, GxP), reducing compliance risk across clinical programs.
- Driving the integration of clinical, omics, imaging, and commercial data into unified analytical platforms, enabling sophisticated analytics and knowledge generation.
- Leading biomarker discovery and validation efforts across multi\-omics (transcriptomics, proteomics, genomics) to support patient stratification and therapeutic advancement.
- Developing and maintaining data infrastructure — data lakes, knowledge graphs, and wearables pipelines — to create sustainable, scalable capabilities for rare disease research.
- Partnering with translational medicine and clinical teams to design innovative Phase 1/2 proof\-of\-concept strategies incorporating omics and digital health technologies.
- Being responsible for the identification and prioritisation of therapeutic targets through AI\-enabled clinical and imaging approaches, advancing candidates to IND.
- Championing the adoption of digital health solutions, including wearables and imaging, as exploratory and registrational endpoints in clinical trials.
- Integrating multi\-omics datasets into pharmacokinetic (PK) models, improving prediction accuracy and strengthening dose\-response insights for rare disease programs.
- Applying AI/ML and omics\-driven disease clustering to guide indication prioritisation and drug re purposing opportunities, expanding pipeline value in rare and metabolic diseases.
- Serving as a senior leader representing Clinical Data Science in internal governance forums and external collaborations, shaping the strategic direction of data\-driven rare disease R\&D.
- Planning and managing complex departmental budget, including forecasting for quarterly, annual, and multi\-year cycles, and input to the Strategic Planning process.
- Leading portfolio\-level data science capacity management for the R\&D unit and contributing to or leading Alexion Quantitative Sciences pivotal initiatives.
Essential Skills/Experience:
- PhD or master’s degree
- Data Science, Bioinformatics, Computational Biology, Computer Science, or equivalent experience
- Minimum 10 years of pharmaceutical or biotech experience
- Experience in a Data Science, Bioinformatics, or AI/ML function
- Minimum 6 years managing direct reports in a global setting
- Experience integrating multi\-omics data (genomics, transcriptomics, proteomics) in drug development and conducting analyses in R, Python, and relevant software
- Deep expertise in AI/ML methods and their application to clinical and translational medicine
- Experience integrating multi\-omics data (genomics, transcriptomics, proteomics) in drug development
- Thorough knowledge of regulatory standards: FDA, 21 CFR Part 11, CDISC, GxP
- Demonstrated ability to influence strategically and build cross\-functional partnerships
- Track record of leading AI and data governance in regulated pharmaceutical environments
- Experience successfully leading global, cross\-functional teams
- Strong eye for business and experience influencing and managing budgets
Desirable Skills/Experience:
- Experience with rare disease drug development and patient stratification strategies
- Familiarity with knowledge graphs, graph ML, and mechanism\-centered disease representation
- Experience with wearables, digital biomarkers, and imaging as clinical endpoints
- Experience with indication expansion and AI\-driven drug re\-purposing programs
- Broad experience across multiple therapeutic areas beyond rare diseases
- Experience managing significant organisational change and scaling data science functions
Where can I find out more?
- Follow AstraZeneca on LinkedIn
- Follow Alexion on LinkedIn
- Follow AstraZeneca on Facebook
- Follow AstraZeneca on Instagram
- Learn more about Alexion at www.alexion.com
At Alexion, you will find a collaborative culture that encourages innovation and a diverse environment where your contributions are valued. You will have the opportunity to be at the forefront of rare disease research and make a meaningful difference in patients' lives.Ready to lead and inspire? and take the first step towards a fulfilling career at Alexion, AstraZeneca Rare Disease.
Total Rewards:
*The annual base pay (or hourly rate of compensation) for this position ranges from $212,994 to $319,492\. 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.*
Alexion embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry\-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non\-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements. Alexion is committed to accommodating persons with disabilities. Such accommodation is available on request in respect of all aspects of the recruitment, assessment and selection process and may be requested by emailing [email protected].
*\#LI\-Hybrid*
Date Posted
09\-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 $212K-$319K 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 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($266K) sits 47% above the category median. Disclosed range: $212K to $319K.
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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