Model jobs in France
PKPD Modeling Pharmacometrics Lead
Mercor
Risk Management - Model and Data Product Manager - Executive Director
JPMorganChase
Machine Learning & Coding Expert (SME) - Freelance Project
Meridial Marketplace, by Invisible
PhD position: The polar-tropical climate connection: ice sheet controls on subtropical hydroclimate
European Geosciences Union e.V.
VP, Chief Information Security Officer
Quadient
Discover more opportunities than anywhere else. Find more jobs nowHead of Prudential Risk
Empirical Search
Anaplan Platform Support Specialist
Aztec Group
Manager, Lead Research Scientist, Training Data (Foundational Research)
Thomson Reuters
Connect with headhunters to apply for similar jobsStaff Software Engineer, Simulation ML Infrastructure
Waymo
Manager, Lead Research Scientist, Training Data (Foundational Research)
TRSS
Manager, Data Science
Workato
Anaplan Platform Support Specialist
Aztec Group
Manager, Lead Research Scientist, LLM Agents (Foundational Research)
Thomson Reuters
Machine Learning Research Engineer (Foundational Research)
TRSS
Manager, Lead Research Scientist, LLM Agents (Foundational Research)
TRSS
Change and Transformation Risk Manager (VN2319)
Rex Technologies GmbH
Security Systems Transformation Lead
Amach
Enterprise Architecture Modeller
Rolls-Royce
Applied AI ML Senior Associate
JPMorgan Chase & Co.
MRC Postdoctoral Research Scientist LMS 2734
UK Research and Innovation
PKPD Modeling Pharmacometrics Lead
Job summary
A leading biotech firm in Greater London is seeking a skilled quantitative pharmacologist with deep experience in PK PD modeling. The ideal candidate will excel in creating and evaluating models, identify non-plausible parameters, and use tools like NONMEM or Monolix. Responsibilities include designing evaluation rubrics and interpreting results to inform decision-making. This is a remote, part-time contract role, offering a flexible approach to outcomes. Candidates with a strong portfolio in exposure-response modeling are encouraged to apply.
Qualifications
- Deep hands-on experience in PK PD exposure-response modeling.
- Expert in identifying non-plausible parameter spaces.
- Demonstrated ability to evaluate dose-exposure predictions.
Responsibilities
- Design micro-evaluations for PK/PD performance.
- Encode quantitative sanity checks into model rubrics.
- Define failure conditions related to model assumptions.
Skills
Education
Tools
Deep hands‑on experience in PK PD exposure-response modeling and ideally population PK or QSP.
Expert at model fitting sensitivity analysis and identifying non‑plausible parameter spaces.
Can evaluate the validity of dose‑exposure predictions and detect high‑risk extrapolations.
Comfortable designing model evaluation rubrics that distinguish between acceptable vs. non‑credible outputs.
Able to articulate how quantitative checks should complement narrative decision logic.
Experience supporting translational or clinical pharmacology leads in dose justification.
Familiarity with integrating nonclinical PK / PD data (2‑species GLP human FIH extrapolation).
812 years of quantitative pharmacology experience in pharma CROs or modeling consultancies.
Strong portfolio in population PK / PD exposure-response and parameter estimation using NONMEM, Monolix or equivalent tools.
Demonstrated ability to interpret model results for decision‑making, not just fit data.
Can create fit‑for‑purpose models and critique model structures or assumptions under uncertainty.
Design and refine micro‑evaluations for PK / PD performance (curve fits, parameter checks, error taxonomies).
Encode quantitative sanity checks into model rubrics for automated evaluation.
Define failure conditions (e.g., unsafe extrapolation, poor coverage, curves, invalid assumptions).
PK / PD datasets, tox summaries and performance prompts (e.g., fit exposure‑response curves, interpret safety margins).
Example model outputs from automated systems.
Quantitative Rubrics: clear thresholds for acceptable parameter fits, coverage curve quality, and model integrity checks.
Golden Fit Examples: representative ideal PK / PD model outputs and visualizations for calibration.
Error Taxonomy: structured list of typical modeling or fitting errors with root‑cause annotations.
Meta‑Layer Commentary: short note per rubric capturing how expert modelers recognize implausible or unsafe fits beyond numeric error values.
- Contract / part‑time remote outcome‑based deliverables.
Full Time
years
1
* The salary benchmark is based on the target salaries of market leaders in their relevant sectors. It is intended to serve as a guide to help Premium Members assess open positions and to help in salary negotiations. The salary benchmark is not provided directly by the company, which could be significantly higher or lower.