Career Category

Job 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.