Senior Scientist: Data Analytics and Pharmacometric Modeling

Career Category

Clinical

Job Description

Senior Scientist, Data Analytics and Pharmacometric Modeling

Join Amgen’s Clinical Pharmacology, Modeling and Simulation (CPMS) team as a Senior Scientist and help shape the future of drug development through data science, predictive analytics, pharmacometrics, and quantitative systems pharmacology. In this role, you will apply innovative modeling and simulation approaches to support critical decisions across clinical development programs and therapeutic areas.

You will work in a collaborative, science-driven environment alongside CPMS colleagues, therapeutic area teams, external partners, and cross-functional R&D stakeholders. This is an opportunity to contribute to model-informed drug development, influence study design and dose selection, and help bring meaningful therapies to patients.

The ideal candidate is a quantitative scientist who enjoys working with complex biological and clinical datasets, translating analyses into actionable insights, and communicating results clearly to multidisciplinary teams. You will have the opportunity to use established pharmacometric methods as well as emerging machine learning and AI-enabled tools to solve high-impact development questions. The typical job responsibilities may include :

• Develop and implement modeling, simulation, and analytics strategies that support clinical drug development programs across Amgen’s portfolio.
• Use pharmacokinetic, pharmacodynamic, patient-level, and disease data to inform dose selection, dosing regimens, study designs, and development decisions.
• Analyze in vitro and preclinical PK/PD data to support first-in-human dose selection and contribute to IND submissions and supporting documentation.
• Build and apply pharmacometric, PK/PD, quantitative systems pharmacology, and predictive analytics models to address key program questions.
• Partner with clinical pharmacologists, statisticians, translational scientists, clinical teams, and other stakeholders to integrate quantitative insights into program strategy.
• Explore and recommend innovative data science, machine learning, and AI-enabled approaches that can improve decision-making and increase development efficiency.
• Communicate modeling results and recommendations clearly through study reports, regulatory documents, presentations, publications, and cross-functional discussions.
• Contribute to the external scientific community through publications, presentations, and professional engagement.

Basic Qualifications

• Doctorate degree and 4 years of relevant experience; or
• Master’s degree and 8 years of relevant experience; or
• Bachelor’s degree and 10 years of relevant experience.

Preferred Qualifications

• Advanced training in statistical or data sciences, computer science, pharmaceutical sciences, engineering, clinical pharmacology, pharmacometrics, or a related quantitative discipline; PhD, MD, PharmD, or equivalent experience preferred.
• Experience in life sciences, biotechnology, pharmaceutical development, consulting, or postdoctoral research, with a strong interest in applying quantitative methods to drug development.
• Hands-on experience developing, scripting, and executing data analytics algorithms, statistical models, or pharmacometric models.
• Experience with tools such as Python, R, MATLAB, NONMEM, SAS, S-Plus, or related modeling and analytics platforms.
• Familiarity with machine learning, AI-enabled methods, or advanced analytics applied to life science datasets is a plus.
• Experience with PK/PD modeling, population-based analyses, clinical trial simulations, or model-informed drug development is highly valued.
• Strong communication skills, including the ability to explain quantitative analyses and recommendations to scientific, clinical, and cross-functional audiences.
• A collaborative mindset, scientific curiosity, and the ability to influence and contribute within multidisciplinary teams.
• A record of scientific contribution through publications, presentations, regulatory support, or external engagement is desirable.

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