Head of In Silico Drug Toxicity, VP of Toxicology
Deep Origin
full-time
Posted on:
Location Type: Remote
Location: United States
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Job Level
About the role
- Own, define, and scale our computational toxicology platform end-to-end.
- Operate as the general manager of the platform with full ownership across scientific vision, technical architecture, product strategy, and execution.
- Work cross-functionally with ML/AI teams, computational biologists, toxicologists, engineers, and commercial teams.
- Translate cutting-edge science into scalable, enterprise-ready systems.
- Define and drive the long-term roadmap for in silico toxicity prediction.
- Design innovative modeling approaches across mechanistic and ML paradigms.
- Integrate ML/AI, systems biology, PK/PD modeling, and real-world data into a unified platform.
- Evaluate tradeoffs between mechanistic modeling and statistical learning approaches.
- Identify breakthrough opportunities beyond current industry standards.
- Translate scientific capabilities into robust, scalable software systems.
- Partner closely with engineering to build secure, enterprise-grade infrastructure.
- Ensure scientific rigor, reproducibility, and regulatory alignment.
- Define product strategy for pharma-facing platform offerings.
- Engage directly with senior R&D and safety leaders at pharmaceutical companies.
- Build and lead a world-class interdisciplinary team (ML scientists, computational biologists, toxicologists, engineers).
Requirements
- 15+ years in computational biology, toxicology, drug discovery, or related domain.
- Proven experience designing and deploying computational models for toxicity or biological systems.
- Demonstrated ability to develop novel modeling approaches beyond industry-standard methods.
- Deep hands-on experience designing and deploying computational toxicology models across mechanistic, statistical, and machine learning paradigms.
- Demonstrated ability to design innovative modeling approaches, not just apply existing frameworks.
- Expertise in integrating ML/AI, systems biology, PK/PD modeling, and real-world data into cohesive predictive systems.
- Strong track record evaluating tradeoffs between mechanistic modeling and statistical/ML approaches.
- Ability to identify breakthrough opportunities beyond current industry standards.
- Experience developing novel model architectures, not just QSAR or legacy approaches.
- Comfortable discussing mechanistic toxicity pathways, ML architectures, regulatory strategy, and platform design.
- Deep expertise in one or more of:
- - Predictive/computational toxicology.
- - Systems pharmacology or systems biology.
- - Mechanistic modeling.
- - ML/AI in drug discovery.
- - PK/PD or ADMET modeling.
- Strong understanding of preclinical safety workflows.
- Familiarity with regulatory frameworks (FDA, EMA, ICH).
- Experience working with complex and proprietary pharma datasets.
- Awareness of data limitations, bias, and validation challenges.
- Experience leading large, interdisciplinary technical teams.
- Ability to operate as both a strategic leader and a technical decision-maker.
- Executive presence with senior pharma stakeholders.
- Strong communication and cross-functional leadership skills.
- Experience building platforms, teams, or systems from zero to scale.
- Comfortable operating in ambiguity and defining direction.
- Strong bias toward action, ownership, and iteration.
Benefits
- Opportunity to define the future of drug safety and predictive toxicology.
- Competitive compensation package with meaningful equity.
- Comprehensive health, dental, and vision coverage.
- Remote-friendly culture with optional onsite work.
- Annual team gatherings and company events.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
computational biologytoxicologydrug discoverycomputational modelsmechanistic modelingstatistical learningmachine learningPK/PD modelingADMET modelingpredictive toxicology
Soft Skills
cross-functional leadershipstrategic leadershiptechnical decision-makingcommunicationteam buildingoperating in ambiguityownershipiterationexecutive presenceidentifying breakthrough opportunities