PK / PD Modeling / Pharmacometrics Lead

This person complements the client’s “Translational / Clinical Pharmacology Decision-Maker” team by grounding dose selection and exposure–response analysis in quantitative structure and parameter plausibility .

Who we’re looking for

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.

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

~8–12 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.

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 exposure–response 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.