PKPD Modeling Pharmacometrics Lead

This person complements the clients Translational / Clinical Pharmacology Decision-Maker team by grounding dose selection and exposureresponse analysis in quantitative structure and parameter plausibility .

Who were looking for

Deep hands-on experience in PK PD exposureresponse modeling and ideally population PK or QSP .

Expert at model fitting sensitivity analysis and identifying non-plausible parameter spaces.

Can evaluate the validity of doseexposure 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.

Nice-to-have :

Experience supporting translational or clinical pharmacology leads in dose justification.

Familiarity with integrating nonclinical PK / PD data (2-species GLP human FIH extrapolation).

Experience level

812 years of quantitative pharmacology experience in pharma CROs or modeling consultancies .

Strong portfolio in population PK / PD exposureresponse 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.

Expectations

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).

Inputs we give :

PK / PD datasets tox summaries and performance prompts (e.g. fit exposureresponse curves interpret safety margins).

Example model outputs from automated systems.

Expected outputs :

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.

Engagement Model & Compensation

• Contract / part-time remote outcome-based deliverables.

Key Skills

Administrative Skills,Facilities Management,Biotechnology,Creative Production,Design And Estimation,Architecture

Employment Type : Full Time

Experience : years

Vacancy : 1