Remotehey

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.