Interested in this AI/ML Engineer role at AstraZeneca?
Apply Now →Skills & Technologies
About This Role
Location Santa Monica, California, United States Job ID R\-253107 Date posted 27/05/2026
We are seeking an experienced leader, Director, Process Data Science and Statistics lead, Process Engineering, within the Cell Therapy Technical Operations function. This role is responsible for end\-to\-end statistical strategy, data infrastructure, and AI/ML capabilities that enable regulatory submissions, process characterization, and commercial manufacturing readiness for pivotal\-stage cell therapy programs.
The Director will own statistical and digital strategies for comparability assessments, process characterization studies, and commercial process monitoring of the cell therapy programs. This role builds and maintains data systems, analytical platforms, and AI\-enabled tooling that connect process characterization and manufacturing execution data to statistical analysis and decision support \- scaling from early process development through late\-stage clinical development and commercial and post\-commercial manufacturing.
This position reports to the Executive Director, Process Engineering, Cell Therapy Development and Operations, and is located in either Santa Monica, CA or Gaithersburg, MD.
Accountabilities
*Statistical Strategy \& Regulatory Support*
- Own end\-to\-end statistical strategy for comparability assessments (site transfers, significant process changes), process characterization, and aids in control strategy development for pivotal\-stage cell therapy programs
- Apply rigorous statistical frameworks (equivalence testing, process capability analysis, variance component analysis, tolerance intervals) to evaluate drug product quality and generate defensible regulatory narratives under tight timelines
- Support authoring CMC sections of IND amendments and regulatory submissions; respond to agency questions on manufacturing data and statistical methods
- Design and analyze DOE studies for process characterization and LVV manufacturing, including risk\-based parameter selection and interaction modeling
- Support process and product characterization for early development activities
*Process Monitoring \& Reporting*
- Design and maintain process monitoring programs for pivotal and commercial manufacturing, including statistical control charts, alert/action limits, and trend detection
- Support CMC process and product characterization analysis during early development
- Build structured interactive reports and dashboards delivering real\-time batch visibility, in\-process trend tracking, and decision support across programs
- Support efforts to incorporate AI and ML technologies during early CMC process development
*Data Infrastructure \& Digital Systems*
- Partner with IT to define requirements for data pipelines and analytical platforms (including tools such as Snowflake, Streamlit applications, other cloud platforms enabling automated reporting) that connect process development and manufacturing execution data to statistical analysis and decision support
- Drive manufacturing data digitization strategy, working with IT and manufacturing sites to establish scalable data capture and integration for commercial readiness
- Collaborate with IT to build and maintain data systems including interactive applications for process simulation, lab equipment data integration, and LLM\-enabled data analysis and review
- Develop and deploy AI/ML solutions for process development and manufacturing process understanding, including predictive modeling, clustering, and automated data analysis workflows
- Support data digitization strategy for process optimization.
*Technical Leadership \& Mentorship*
- Shape long\-term digital manufacturing and data science strategy, driving AI/ML integration and scalable infrastructure to enable commercial readiness
- Support data science strategy and strategic plans for incorporating AI/ML during early CMC Process Development.
- Mentor scientists and engineers in statistical rigor, DOE principles, and applied data science to elevate organizational capability
- Collaborate cross\-functionally with R\&D, MSAT, Manufacturing Operations, Quality, and IT to align data capabilities with program needs
Required Qualifications
*Education*
- Ph.D. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or related field with 6\+ years of industry experience
- OR M.S. in Chemical Engineering, Biochemical Engineering, Biotechnology, Data Science, Statistics, or related field with 10\+ years of industry experience
- OR B.S. with 12\+ years of hands\-on industry experience
*Technical Skills*
- Demonstrated expertise in applied data science and statistical methods for process development or manufacturing (equivalence testing, process capability, variance components, DOE, control charting)
- Proficiency in Python (pandas, scipy, statsmodels, scikit\-learn, plotly) and SQL for data analysis and pipeline development
- Experience with software development practices including version control, automation, and reproducible analysis workflows
- Experience supporting regulatory submissions (IND, BLA) with statistical analysis and CMC documentation
- Strong communication skills with ability to translate complex statistical findings into actionable insights for cross\-functional audiences and regulatory\-ready narratives
*Soft Skills*
- Excellent cross\-functional communication and collaboration in matrixed environments
- Ability to work independently, set priorities, and deliver under time\-sensitive regulatory timelines
- Strategic thinking with ability to shape long\-term data and analytics roadmaps
Preferred Qualifications
- 5\+ years of experience working with cloud data systems (AWS, Snowflake, Databricks, or similar)
- Experience building interactive analytical applications (Streamlit, Dash, or similar)
- Experience with AI/ML methods applied to manufacturing or bioprocess problems, including LLM\-based tooling
- Experience in cell therapy or biologics manufacturing
- Experience with DOE design and analysis for process characterization
- Track record of building and deploying automated reporting and process monitoring systems
When we put unexpected teams in the same room, we fuel 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 salary for this position ranges from $161,252\.80 \- $241,879\.20\. However, base pay offered may vary depending on multiple individualized factors, including market location, job\-related experience. If hired, employee will be in an "at\-will position" and we reserve the right to modify base salary (and 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.
Why AstraZeneca?At AstraZeneca's Biopharmaceuticals R\&D division, we are nimble and agile, always harnessing our diverse knowledge. We are part of the solution, turning our drug development strategies into reality. Work at all stages of development to translate our life\-changing science into medicines to get the best results for AstraZeneca, patients in need and healthcare professionals. We are a diverse and open\-minded team harnessing our different skills and experiences. Our differences enable us to explore new ideas and ways of doing things. It keeps us on our toes and excited for what's next.
Ready to make a difference? Apply today and join us in our mission to create life\-changing medicines!
So, what’s next!
*Are you ready to bring new ideas and fresh thinking to the table? Brilliant! We have one seat available and we hope it’s yours.*
Where can I find out more?
Check out our landing page for more information on our BPD group https://careers.astrazeneca.com/bpd
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
Date Posted
28\-May\-2026
Closing Date
18\-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 $161K-$241K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2088 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 4,021 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $180,000 based on 12,397 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,800. This role's midpoint ($201K) sits 12% above the category median. Disclosed range: $161K to $241K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
AstraZeneca AI Hiring
AstraZeneca has 9 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boston, MA, US, Gaithersburg, MD, US, Wilmington, DE, US. Compensation range: $174K - $319K.
Location Context
Across all AI roles, 15% (608 positions) offer remote work, while 3,392 require on-site attendance. Top AI hiring metros: New York (2,585 roles, $210,300 median); San Francisco (2,102 roles, $253,000 median); Los Angeles (1,764 roles, $190,500 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 4,021 open positions tracked in our dataset. By seniority: 118 entry-level, 1,906 mid-level, 1,555 senior, and 442 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (608 positions). The remaining 3,392 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 4,021 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,818), Data Scientist (312), AI Software Engineer (280). 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 (118) are outnumbered by mid-level (1,906) and senior (1,555) 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 442 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (608 positions), with 3,392 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 $200,000. Top-quartile roles start at $253,000, 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 $290,000 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 (2,069 postings), Aws (1,260 postings), Azure (946 postings), Rag (893 postings), Gcp (783 postings), Pytorch (624 postings), Prompt Engineering (619 postings), Claude (570 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.