Director, Computational DMPK – Drug Metabolism & Pharmacokinetics

Deep Origin

. Lead a team of computational scientists and data curators across sites; set scientific direction, priorities and review standards, and develop team members.

Posted 11/7/2026full-timeRemote • United States, United KingdomLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in drug metabolism and computational modeling, with a strong focus on PBPK model development and validation. Proven ability to lead scientific teams, manage complex projects, and communicate effectively with stakeholders.

Highest-signal resume keywords

PhD In Computational ChemistryDrug Metabolism ExpertiseComputational ModelingPBPK Model DevelopmentTeam Leadership

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Applicant Tracking System Keywords

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Hard Skills

Drug MetabolismEnzyme KineticsMachine LearningModel ValidationParameter EstimationBayesian InferenceODE-Based ModelsClearance ScalingMetabolite IdentificationData Curation

Soft Skills

Clear CommunicationTeam ManagementProject Management

Tools & Technologies

SimcypGastroPlusPK-SimSimBiologyPython

Industry Keywords

Regulatory ScienceFDA GuidanceModel-Informed Drug DevelopmentMetabolomicsIntrinsic Clearance

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Lead a team of computational scientists and data curators across sites; set scientific direction, priorities and review standards, and develop team members.
Own the roadmap and release gates for four metabolism models for the VHAT platform, standalone products and external review.
Own metabolism-to-PBPK integration, including parameter transfer, clearance scaling to in vivo hepatic clearance, and validation against clinical PK and drug-drug interaction data.
Ensure parent and metabolite exposure flows correctly into organ models.
Design rigorous validation experiments with leakage-aware data splits, applicability-domain and confidence reporting, locked prospective predictions, and external, blinded and independent benchmarks.
Set data strategy, plan curation targets and throughput, oversee AI-assisted literature mining and quality control, support data licensing, and design wet-lab experiments with partners.
Extend model coverage to Phase II enzymes, FMO, AOX, multistep metabolic networks, enzyme polymorphism and population variability.
Manage relationships with consortium partners, contract research organizations, academic reviewers and independent validation teams.
Author technical milestone reports and present results to ARPA-H.
Contribute metabolism content to grant proposals and regulatory engagement on new approach methodologies.
Carry uncertainty from metabolism models through PBPK to final organ-level risk outputs.
Within six months, take ownership of the four models and team, release metabolite identity and enzyme-isoform predictions, pass clearance and kinetic parameters into PBPK, score prospective predictions, establish a kinetics model and define curation and coverage plans.
Within twelve months, support independent validation, feed parent and metabolite concentration profiles into PBPK and organ models, expand enzyme coverage, propagate confidence estimates, and operate the team against a defined roadmap.

Requirements

What you’ll need

A PhD in computational chemistry, cheminformatics, drug metabolism, computational biology, pharmacology or a related field.
Deep, hands-on expertise in drug metabolism, including CYP and non-CYP enzymes, sites and products of metabolism, enzyme kinetics and clearance, and assessing whether predictions are chemically and biologically sound.
Strong track record in computational modeling, including machine learning for molecular property or reactivity prediction, mechanistic or kinetic modeling, ODE-based models, parameter estimation, Bayesian or frequentist inference, and fluent Python use.
Demonstrated rigor in model validation, including appropriate data splits, applicability domains, external or blinded benchmarks, and explaining model limitations.
Experience leading a scientific team and delivering against milestones and deadlines.
Clear written and spoken communication with scientists, partners and funders.
Strongly preferred: hands-on experience building or parameterizing PBPK models in Simcyp, GastroPlus, PK-Sim, SimBiology or a custom ODE framework.
Strongly preferred: experience translating in vitro or predicted intrinsic clearance into in vivo hepatic clearance and human PK, including IVIVE scaling factors.
Strongly preferred: experience using PBPK to assess metabolic drug-drug interactions, such as CYP inhibition and induction.
Strongly preferred: experience combining machine learning predictions with mechanistic PK models and carrying uncertainty into downstream predictions.
Nice to have: experience with metabolomics or LC-MS metabolite identification data.
Nice to have: experience running data curation at scale, including annotation standards and quality control.
Nice to have: experience on government-funded research programs with milestone-based reporting.
Nice to have: exposure to regulatory science, such as FDA guidance on new approach methodologies, model-informed drug development or the ISTAND program.
Nice to have: familiarity with medicinal chemistry and addressing metabolism liabilities during lead optimization.
Must be authorized to work for any employer in the U.S.; employment visa sponsorship or transfer is unavailable.

Benefits

Comp & perks

Competitive compensation package with meaningful equity.
Comprehensive health, dental, and vision coverage.
A remote-friendly culture across the U.S. and Europe, with flexible, cross-time-zone working.
Opportunity to shape the future of health, longevity, and our ability to simulate life.
Annual team gatherings and company events.