Description
**What you will do**
Let’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 Responsibilities**
+ 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
**What we expect of you**
We 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 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
Or
Master’s degree and 8+years of directly related experience
**Preferred Qualifications**
+ Experience developing PBPK, TMDD, PK/PD, exposure-response, quantitative systems pharmacology, or other mechanistic models for biologics or therapeutic discovery
+ Strong understanding of biologics pharmacology, target-mediated drug disposition, translational scaling, and cross-species extrapolation
+ Experience working with scientific, preclinical, translational, or literature-derived data sources in a data-governed environment
+ Proficiency with scientific computing and modeling tools such as R, Python, MATLAB, NONMEM, Monolix, mrgsolve, Stan, PyMC, SimBiology, or related platforms
+ Experience with model calibration, parameter estimation, sensitivity analysis, uncertainty quantification, simulation-based study design, and model documentation
+ Ability to translate quantitative models, assumptions, and uncertainty into clear recommendations for cross-functional scientific stakeholders
+ Experience supporting biologics, antibodies, protein therapeutics, translational science, quantitative pharmacology, or early drug discovery
+ Familiarity with reproducible scientific computing practices, including version control, workflow automation, code review, testing, documentation, and data provenance
+ 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.





