AstraZeneca
Associate Director, Systems Medicine
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Location: The Discovery Center (DISC), Cambridge Biomedical Campus, UK
Salary: Competitive Salary and Benefits!
Introduction to the role
Are you ready to turn mechanistic models into dose and schedule strategies that protect patients and accelerate development? Join a team of specialist modelers who operate with high visibility and real decision-making influence, shaping clinical strategy across therapy areas. Based at our Discovery Centre in Cambridge, UK, you will work in a dynamic, multidisciplinary environment spanning nonclinical and clinical phases.
About the role
The Systems Medicine group is seeking a Systems Modeler passionate about using mathematical and computational skills to develop and apply empirical and/or mechanistic models of Pharmacology and Toxicology. The group is under Clinical Pharmacology & Quantitative Pharmacology Department and consists of ~20 mathematical modelers with backgrounds in applied biomathematics, computational biology, and/or biomedical/chemical engineering.
Working in a dynamic, multidisciplinary environment, the successful candidate will support projects in both non-clinical and clinical phases. The candidate will develop and apply pharmacological mechanistic systems models to contribute to decisions on dose regimens by balancing efficacy and safety via modelling & simulation based on the understanding of the mechanism of action of investigational drugs. The role will include opportunities to develop and apply Quantitative Systems Pharmacology (QSP) and Toxicology (QST) models, including incorporation of virtual populations to support translational decision-making and dose/schedule selection. In addition, the incumbent will develop QSP&T models based on Microphysiological Systems (organ-on-chips and organoids).
To succeed in this role, we believe you have drug development experience and you are a person who enjoys working collaboratively with a variety of key stakeholders and collaborators to identify opportunities, build support and deliver innovative modelling and simulation solutions. Experience or exposure in modalities such as immune cell engagers, antibody-drug conjugates (ADCs), and radioconjugates (RCs) would be valuable.
Main responsibilities
- Create, expand or refine mathematical models to address drug-discovery and nonclinical/clinical development questions
- Lead compound-specific projects with hands-on analysis by choosing the best modelling approach to address questions
- Contribute to the design, execution, and interpretation of clinical studies
- Develop and apply clinical QSP&T models, including virtual population approaches, to support prediction of efficacy, safety, and dose regimens in clinical development
- Test and adopt existing modelling platforms
- Review modelling works by colleagues, ensuring high-quality standards
- Contribute to AZ drug development with innovative ideas
- Stay informed with emerging literature and science in modelling and simulation sciences, including developments in clinical QSP&T models, virtual populations, and digital twin approaches
- Collaborate well within the Systems Medicine group and cross-functional teams
- Guide junior modelers
- Represent AZ by publication, podium presentations, and/or organization of symposia
Essential requirements
- PhD or similar degree in chemical, mechanical or biomedical engineering, physics, applied mathematics or related field
- Experience working in the industry and postdoctoral experience in building, validating, and using predictive mechanistic mathematical models for drug development. (Ideally, 4 years of experience).
- At least 3 published papers
- Excellent understanding of theory, principles and statistical aspects of mathematical modelling and simulation, including numerical methods, parametrization and ODEs.
- Knowledge of models of biological pathways/systems to support translational research.
- Hands-on knowledge of modelling with ODEs, Agent-Based Modelling, Statistical and/or Machine Learning modelling, etc
- Aptitude and experience to influence decisions and experimental design by using available data and appropriate modelling solutions
- Self-directed, independent, and highly-motivated researcher who excels in a collaborative, multi-disciplinary environment.
- Evidence of identifying, developing, and applying innovative solutions to scientific and technological problems faced in systems and predictive modelling
- Familiarity with the challenges of drug discovery and forward thinking with respect to the general application of mathematical models in drug discovery and development
- Excellent oral and written communication skills and the ability to interact effectively with scientists in other subject areas with a positive and collaborative attitude
- Experience with data analysis tools and languages such as Matlab and/or Python.
- Ability to learn new areas of biological sciences and build on solid foundation of quantitative skills to develop models.
- Ability to keep up to date with and propose the implementation of scientific and technological developments.
- Ability to interact across pre-clinical and clinical teams.
- Ability to keep up with new modelling approaches and propose implementation of scientific and technological developments in the areas of QSP&T
- Experience in linking QSP&T and pharmacokinetics to predict safe and efficacious doses
- Experience or demonstrated exposure to clinical-stage QSP modelling, ideally including use of virtual populations to characterize variability and support clinical decision-making
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Preferred Skills and Qualifications
- Exposure to current principles and concepts in DMPK, Toxicology and Safety
- Experience with PK, PKPD, TKTD modelling and joint longitudinal modelling tools or any other relevant software.
- Familiarity with the challenges of drug discovery and forward thinking with respect to the general application of mathematical models in discovery and development.
- Evidence of identifying, developing, and applying innovative solutions to scientific and technological problems faced in systems and predictive modelling.
- Experience with clinical QSP&T applications in oncology or related therapeutic areas
- Experience incorporating virtual populations using mechanistic models
- Exposure to digital twin concepts or approaches in clinical/translational modelling
- Experience or familiarity with immune cell engagers, ADCs, radioconjugates (RCs) and/or Microphysiological System data analysis
Why AstraZeneca
Here you will be recognized as an expert working at the forefront of science, with the visibility and trust to make consequential decisions for patients and the business. We bring unexpected teams into the same room to unleash bold thinking, blending deep quantitative skills with clinical, biology, and safety expertise. Our environment is both supportive and stretching: leaders back smart risk-taking, and we treat setbacks as data to learn from. You will work across multiple therapy areas, in a fast-growing company investing heavily in data and technology, all while anchoring your impact in a clear purpose—advancing medicines that change lives.
We value kindness alongside ambition, and we celebrate work that improves how decisions are made for patients. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
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Date Posted 08-Jul-2026
Closing Date 15-Aug-2026
Our mission
We are committed to building an inclusive and equitable environment. We want people to feel they belong at AstraZeneca and Alexion, starting with our recruitment process. We welcome and consider applications from all qualified candidates, regardless of characteristics. We offer reasonable adjustments/accommodations to help all candidates to perform at their best. If you have a need for any adjustments/accommodations, please complete the section in the application form.
About AstraZeneca
AstraZeneca is a global, science-led, patient-focused biopharmaceutical company. We focus on discovering, developing and commercialising prescription medicines for some of the world’s most serious diseases. But we are more than one of the world’s leading pharmaceutical companies. At AstraZeneca, we’re dedicated to being a Great Place to Work. Where you are empowered to push the boundaries of science, challenge convention and unleash your entrepreneurial spirit. To embrace differences and take bold actions to drive the change needed to meet global healthcare and sustainability challenges. There is no better place to make a difference in medicine, patients, and society. An inclusive culture where you will connect different thinking to generate new and valuable opportunities. Where you will find a commitment to lifelong learning, growth and development for all. Our Inclusion & Diversity (I&D) mission is to create an inclusive and equitable environment where people belong, using the power of our diversity to push the boundaries of science to deliver life-changing medicines to patients.
Inclusion and diversity are fundamental to the success of our company, because innovation requires breakthrough ideas that only come from a diverse workforce empowered to challenge conventional thinking.
What we offer
We’re curious about science and the advancement of knowledge. We find creative ways to approach new challenges. We’re driven to make the right choices and be accountable for our actions. As an organisation centred around what makes us human, we put a big focus on people. Across our business, we want colleagues to wake up excited about their day at the office, in the field, or in the lab. Along with our purpose to bring life-changing medicines to people across the globe, we have a promise to you: to help you realise the full
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