Job ID R-255280
Date posted 06/26/2026
On average, it takes more than 10 years to develop a drug and costs more than $1.3 billion. Over 70% of drug R&D costs are spent on clinical development, yet the success rate from phase I to approval is only around 10%. At Evinova, a new health tech business as part of the AstraZeneca Group, we are on a mission to increase clinical trial success rates by 20%, accelerate clinical development timelines by 36 months, and reduce study costs by 50% by leveraging state-of-the-art AI and Machine Learning.
If you are a rigorous modeler with hands-on experience designing and validating ML solutions, a strong grounding in statistics and modern machine learning, a talent for turning data into trustworthy decisions, and a voracious learner, then you could be a fantastic fit for our team.
Talent with ambition to grow into leadership roles will be a key differentiator for successful candidates. Glass ceiling smashers especially welcome.
We are seeking a highly skilled and innovative Principal AI&ML Scientist whose primary mission is to work hand in hand with our Clinical Operations and Clinical Development teams: to understand their problems deeply and design AI and advanced analytics solutions that significantly reduce the complexity of clinical trials. You will frame these problems quantitatively, design and run experiments, and apply proven statistical and machine learning methods to turn complex clinical and product data into trustworthy decisions. You’ll own model evaluation and clinical validation – from endpoints and study analysis to real-world evidence – and partner with engineering to bring validated approaches into production, always grounding your work in real clinical needs.
Over time, you will become the subject-matter expert within the AI & Data Science team on clinical trial design and the processes that surround it, staying closely connected to science, clinicians, and medical experts so that everything the team builds is grounded in clinical reality. This position offers the opportunity to set the methodological bar for the team, mentor others in experimental rigor, and – at the senior end of the role – develop novel approaches when established methods fall short. You’ll be at the forefront of applying machine learning and statistical science to revolutionize clinical development and drug discovery.
WHAT THE ROLE INVOLVES
You will:
Apply proven statistical and machine learning methods with rigor – experimental design, hypothesis testing, causal inference, and uncertainty quantification.
Own model evaluation and clinical validation, including endpoints, overread, and real-world evidence.
Translate analytical results into clear, trustworthy product and clinical decisions, and communicate them to technical, non-technical, and clinical stakeholders.
SKILLS AND CAPABILITIES NEEDED
Qualifications:
Degree in Statistics, Computer Science, Mathematics, Physics, or a related quantitative field; an advanced degree is preferred.
Strong Python programming with proficiency in data science libraries such as NumPy, pandas, scikit-learn, SciPy, statsmodels, Optuna, and TensorFlow/PyTorch.
Strong foundation in statistics and experimental design, including hypothesis testing, causal inference, and uncertainty quantification.
Rigorous model evaluation and validation methodology, including handling of bias, data leakage, and generalization.
Hands-on experience with generative AI and large language models (LLMs) – calling and integrating LLM APIs, prompt engineering, retrieval-augmented generation (RAG), and evaluating LLM outputs for practical applications.
Desirable Skills/Experience
Knowledge of drug development, clinical trial design, and clinical operations; prior experience working in the pharmaceutical industry is nice to have but not required.
Why Evinova (AstraZeneca)?
Evinova draws on AstraZeneca’s deep experience developing novel therapeutics, informed by insights from thousands of patients and clinical researchers. Together, we can accelerate the delivery of life-changing medicines, improve the design and delivery of clinical trials for better patient experiences and outcomes, and think more holistically about patient care before, during, and after treatment. We know that regulators, healthcare professionals, and care teams at clinical trial sites do not want a fragmented approach. They do not want a future where every pharmaceutical company provides its own, different digital solutions. They want solutions that work across the sector, simplify their workload, and benefit patients broadly. By bringing our solutions to the wider healthcare community, we can help build more unified approaches to how we all develop and deploy digital technologies, better serving our teams, physicians, and ultimately patients. Evinova represents a unique opportunity to deliver meaningful outcomes with digital and AI to serve the wider healthcare community and create new standards for the sector. Join us on our journey of building a new kind of health tech business to reset expectations of what a bio-pharmaceutical company can be. This means we’re opening new ways to work, pioneering cutting-edge methods, and bringing unexpected teams together. Interested? Come and join our journey.
So, what’s next?
Are you already imagining yourself joining our team? Good, because we can’t wait to hear from you.
Where can I find out more?
Our Social Media, Follow Evinova on LinkedIn https://www.linkedin.com/company/evinova/
Learn more about Evinova www.evinova.com
Date Posted
25-jun-2026
Closing Date
08-jul-2026
AstraZeneca 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.
