Remote PK/PD Modeling / Pharmacometrics Lead - AI Trainer ($150-$200 per hour)Mercor • Athens, Georgia, US
Remote PK / PD Modeling / Pharmacometrics Lead - AI Trainer ($150-$200 per hour)
Mercor • Athens, Georgia, US
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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
###
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. ###
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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