Remote PK/PD Modeling / Pharmacometrics Lead - AI Trainer ($150-$200 per hour)Mercor • McKinney, Texas, US
Remote PK / PD Modeling / Pharmacometrics Lead - AI Trainer ($150-$200 per hour)
Mercor • McKinney, Texas, US
[job_card.variable_days_ago]
[job_preview.job_type]
- [job_card.part_time]
- [filters.remote]
[job_card.job_description]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.
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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Ai Lead • McKinney, Texas, US
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