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Remote PK/PD Modeling / Pharmacometrics Lead - AI Trainer ($150-$200 per hour)
Remote PK/PD Modeling / Pharmacometrics Lead - AI Trainer ($150-$200 per hour)Mercor • Green Bay, Wisconsin, US
Remote PK / PD Modeling / Pharmacometrics Lead - AI Trainer ($150-$200 per hour)

Remote PK / PD Modeling / Pharmacometrics Lead - AI Trainer ($150-$200 per hour)

Mercor • Green Bay, Wisconsin, US
[job_card.variable_days_ago]
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  • [job_card.part_time]
  • [filters.remote]
[job_card.job_description]

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.
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