Principal Scientist - PKDM, Modeling and Simulation
Amgen
9 hours ago
Remote friendly (South San Francisco, CA)
United States
Clinical Research and Development
Principal Scientist-PKDM, Modeling and Simulation
What You Will Do
Contribute to Amgen’s drug discovery and development portfolio with a focus on understanding pharmacokinetics (PK) and pharmacodynamics (PD) for small and large molecule drug candidates across therapeutic areas. Support PK/PD-related and mathematical modeling activities in the PKDM space through target/compound evaluation, preclinical study design, exposure-response investigations, translational strategies, and influencing clinical study design.
Key Responsibilities
- Devise and implement quantitative analyses and strategies for drug development decision-making using mechanistic and translational modeling
- Collaborate with PKDM and multi-disciplinary teams to generate knowledge that informs quantitative strategies
- Build hands-on fit-for-purpose models (PK-PD, QSP, PBPK) using specialized modeling tools; apply AI/ML to modernize PK-PD modeling
- Document and archive data analyses, models, presentations, and reports
- Summarize findings and communicate conclusions to project and cross-functional teams
- Build collaborations with peers and stakeholders (e.g., chemistry, biology, toxicology, biomarkers, protein scientists)
Basic Qualifications
- PhD OR PharmD OR MD with 2 years applying quantitative PK/PD principles; or Master’s with 5 years; or Bachelor’s with 7 years.
Preferred Qualifications/Skills
- PhD in relevant fields with 2+ years in drug discovery/development
- Systems modeling/simulation track record for strategic decisions
- Proficiency with mechanistic modeling approaches (e.g., differential equations, nonlinear mixed effects, stochastic/agent-based)
- Hands-on experience with tools/programming (e.g., R, Simbiology, MATLAB, R, C)
- Critical thinking and ability to communicate across disciplines; self-driven and flexible.
Application Instructions
- Apply via careers.amgen.com. No application deadline; applications accepted until a sufficient number are received or a candidate is selected.
What You Will Do
Contribute to Amgen’s drug discovery and development portfolio with a focus on understanding pharmacokinetics (PK) and pharmacodynamics (PD) for small and large molecule drug candidates across therapeutic areas. Support PK/PD-related and mathematical modeling activities in the PKDM space through target/compound evaluation, preclinical study design, exposure-response investigations, translational strategies, and influencing clinical study design.
Key Responsibilities
- Devise and implement quantitative analyses and strategies for drug development decision-making using mechanistic and translational modeling
- Collaborate with PKDM and multi-disciplinary teams to generate knowledge that informs quantitative strategies
- Build hands-on fit-for-purpose models (PK-PD, QSP, PBPK) using specialized modeling tools; apply AI/ML to modernize PK-PD modeling
- Document and archive data analyses, models, presentations, and reports
- Summarize findings and communicate conclusions to project and cross-functional teams
- Build collaborations with peers and stakeholders (e.g., chemistry, biology, toxicology, biomarkers, protein scientists)
Basic Qualifications
- PhD OR PharmD OR MD with 2 years applying quantitative PK/PD principles; or Master’s with 5 years; or Bachelor’s with 7 years.
Preferred Qualifications/Skills
- PhD in relevant fields with 2+ years in drug discovery/development
- Systems modeling/simulation track record for strategic decisions
- Proficiency with mechanistic modeling approaches (e.g., differential equations, nonlinear mixed effects, stochastic/agent-based)
- Hands-on experience with tools/programming (e.g., R, Simbiology, MATLAB, R, C)
- Critical thinking and ability to communicate across disciplines; self-driven and flexible.
Application Instructions
- Apply via careers.amgen.com. No application deadline; applications accepted until a sufficient number are received or a candidate is selected.