PK​/PD Modeling​/Pharmacometrics Lead

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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
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• 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).

• 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.

• 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.

Not Applicable

Full‑time

Other

Software Development