Senior Data Scientist
Amgen
Senior Data Scientist
Job Description
Career CategoryClinicalJob DescriptionWhat you will doLet's do this. Let's change the world. We are seeking a Senior Data Scientist with expertise in quantitative pharmacology, PBPK/PK/PD modeling, and translational simulation to support biologics discovery. In this vital role, you will develop and apply fit-for-purpose mechanistic modeling and simulation approaches that help discovery teams interpret complex biological and pharmacology information, generate testable hypotheses, and make data-driven decisions. You will work with appropriate scientific data in secure, governed environments while ensuring modeling assumptions, documentation, and outputs are reproducible and decision-ready.The successful candidate will work in a collaborative, multidisciplinary environment, partnering with scientists, data scientists, translational modelers, pharmacology experts, and discovery teams to design modeling strategies, evaluate uncertainty, and translate quantitative insight into actionable recommendations. Key ResponsibilitiesDevelop and apply PBPK, TMDD, PK/PD, exposure-response, and related quantitative models for biologics discovery and translational questionsTranslate complex scientific information into quantitative assumptions, scenarios, and simulations that support model-informed decision-makingBuild reproducible workflows for parameter estimation, model calibration, sensitivity and uncertainty analysis, documentation, and reviewApply statistical, machine learning, or Bayesian methods where appropriate to support parameter inference, model updating, and scenario analysisPartner with experimental and translational teams to align modeling plans, interpret results, and identify fit-for-purpose data needsCommunicate modeling assumptions, limitations, uncertainty, findings, and recommendations clearly through reports, visualizations, and presentationsContribute to scalable model-informed drug design practices and reusable modeling frameworks that can support biologics discovery programs What we expect of youWe are all different, yet we all use our unique contributions to serve patients. The collaborative professional we seek is a Senior Data Scientist with these qualifications. Basic QualificationsDoctorate degree with 4+yrs in Pharmacometrics, Quantitative Pharmacology, Pharmacokinetics, Bioengineering, Biomedical Engineering, Computational Biology, Applied Mathematics, Statistics, Data Science, or a related fieldOrMaster's degree and 8+years of directly related experiencePreferred QualificationsExperience developing PBPK, TMDD, PK/PD, exposure-response, quantitative systems pharmacology, or other mechanistic models for biologics or therapeutic discoveryStrong understanding of biologics pharmacology, target-mediated drug disposition, translational scaling, and cross-species extrapolationExperience working with scientific, preclinical, translational, or literature-derived data sources in a data-governed environmentProficiency with scientific computing and modeling tools such as R, Python, MATLAB, NONMEM, Monolix, mrgsolve, Stan, PyMC, SimBiology, or related platformsExperience with model calibration, parameter estimation, sensitivity analysis, uncertainty quantification, simulation-based study design, and model documentationAbility to translate quantitative models, assumptions, and uncertainty into clear recommendations for cross-functional scientific stakeholdersExperience supporting biologics, antibodies, protein therapeutics, translational science, quantitative pharmacology, or early drug discoveryFamiliarity with reproducible scientific computing practices, including version control, workflow automation, code review, testing, documentation, and data provenanceStrong scientific communication skills, with peer-reviewed publications in venues such as CPT: Pharmacometrics & Systems Pharmacology, Journal of Pharmacokinetics and Pharmacodynamics, Clinical Pharmacokinetics, or comparable journals; candidates are encouraged to highlight representative publications on their resume..
Locations
- India - Hyderabad
Skills Required
- scientific computingintermediate
- model calibrationintermediate
- reproducible scientific computing practicesintermediate
Required Qualifications
- Doctorate degree with 4+yrs in Pharmacometrics, Quantitative Pharmacology, Pharmacokinetics, Bioengineering, Biomedical Engineering, Computational Biology, Applied Mathematics, Statistics, Data Science, or a related field (degree in pharmacometrics)
- Master's degree and 8+years of directly related experience (experience, 8 years)
Preferred Qualifications
- Experience developing PBPK, TMDD, PK/PD, exposure-response, quantitative systems pharmacology, or other mechanistic models for biologics or therapeutic discovery (experience)
- Strong understanding of biologics pharmacology, target-mediated drug disposition, translational scaling, and cross-species extrapolation (experience)
- Experience working with scientific, preclinical, translational, or literature-derived data sources in a data-governed environment (experience)
- Proficiency with scientific computing and modeling tools such as R, Python, MATLAB, NONMEM, Monolix, mrgsolve, Stan, PyMC, SimBiology, or related platforms (experience)
- Experience with model calibration, parameter estimation, sensitivity analysis, uncertainty quantification, simulation-based study design, and model documentation (experience)
- Ability to translate quantitative models, assumptions, and uncertainty into clear recommendations for cross-functional scientific stakeholders (experience)
- Experience supporting biologics, antibodies, protein therapeutics, translational science, quantitative pharmacology, or early drug discovery (experience)
- Familiarity with reproducible scientific computing practices, including version control, workflow automation, code review, testing, documentation, and data provenance (experience)
- Strong scientific communication skills, with peer-reviewed publications in venues such as CPT: Pharmacometrics & Systems Pharmacology, Journal of Pharmacokinetics and Pharmacodynamics, Clinical Pharmacokinetics, or comparable journals; candidates are encouraged to highlight representative publications on their resume. (experience)
- Experience developing PBPK, TMDD, PK/PD, exposure-response, quantitative systems pharmacology, or other mechanistic models for biologics or therapeutic discovery (experience)
- Strong understanding of biologics pharmacology, target-mediated drug disposition, translational scaling, and cross-species extrapolation (experience)
- Experience working with scientific, preclinical, translational, or literature-derived data sources in a data-governed environment (experience)
- Proficiency with scientific computing and modeling tools such as R, Python, MATLAB, NONMEM, Monolix, mrgsolve, Stan, PyMC, SimBiology, or related platforms (experience)
- Experience with model calibration, parameter estimation, sensitivity analysis, uncertainty quantification, simulation-based study design, and model documentation (experience)
- Ability to translate quantitative models, assumptions, and uncertainty into clear recommendations for cross-functional scientific stakeholders (experience)
- Experience supporting biologics, antibodies, protein therapeutics, translational science, quantitative pharmacology, or early drug discovery (experience)
- Familiarity with reproducible scientific computing practices, including version control, workflow automation, code review, testing, documentation, and data provenance (experience)
- Strong scientific communication skills, with peer-reviewed publications in venues such as CPT: Pharmacometrics & Systems Pharmacology, Journal of Pharmacokinetics and Pharmacodynamics, Clinical Pharmacokinetics, or comparable journals; candidates are encouraged to highlight representative publications on their resume. (experience)
Responsibilities
- The successful candidate will work in a collaborative, multidisciplinary environment, partnering with scientists, data scientists, translational modelers, pharmacology experts, and discovery teams to design modeling strategies, evaluate uncertainty, and translate quantitative insight into actionable recommendations.
- Develop and apply PBPK, TMDD, PK/PD, exposure-response, and related quantitative models for biologics discovery and translational questions
- Translate complex scientific information into quantitative assumptions, scenarios, and simulations that support model-informed decision-making
- Build reproducible workflows for parameter estimation, model calibration, sensitivity and uncertainty analysis, documentation, and review
- Apply statistical, machine learning, or Bayesian methods where appropriate to support parameter inference, model updating, and scenario analysis
- Partner with experimental and translational teams to align modeling plans, interpret results, and identify fit-for-purpose data needs
- Communicate modeling assumptions, limitations, uncertainty, findings, and recommendations clearly through reports, visualizations, and presentations
- Contribute to scalable model-informed drug design practices and reusable modeling frameworks that can support biologics discovery programs
- Develop and apply PBPK, TMDD, PK/PD, exposure-response, and related quantitative models for biologics discovery and translational questions
- Translate complex scientific information into quantitative assumptions, scenarios, and simulations that support model-informed decision-making
- Build reproducible workflows for parameter estimation, model calibration, sensitivity and uncertainty analysis, documentation, and review
- Apply statistical, machine learning, or Bayesian methods where appropriate to support parameter inference, model updating, and scenario analysis
- Partner with experimental and translational teams to align modeling plans, interpret results, and identify fit-for-purpose data needs
- Communicate modeling assumptions, limitations, uncertainty, findings, and recommendations clearly through reports, visualizations, and presentations
- Contribute to scalable model-informed drug design practices and reusable modeling frameworks that can support biologics discovery programs
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