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About the Role
Lead the development and application of digital twins to improve clinical trial design and support evidence-based clinical development decisions. This role prioritizes digital-twin, simulation, and AI/ML expertise, supported by clinical-development experience and close partnership with trial-design experts.
Responsibilities
- Lead the design, development, and evaluation of digital twins of patients, disease trajectories, treatment response, or clinical trials using clinical trial and relevant external data.
- Combine mechanistic, statistical, causal, and machine learning approaches to create fit-for-purpose digital twins that can be simulated under alternative populations, interventions, dosing strategies, endpoints, or trial designs.
- Define use cases and contexts of use with clinical and quantitative experts so each digital twin addresses a specific trial-design or development decision.
- Assess calibration, validation, robustness, transportability, uncertainty, and practical relevance using appropriate internal and external evaluation strategies.
- Develop reproducible workflows and reusable digital-twin components that can be applied across programs or therapeutic areas.
- Partner with trial teams to frame development questions and evaluate design options, including target populations, eligibility criteria, enrichment strategies, comparators, endpoints, visit schedules, dosing strategies, and operational tradeoffs.
- Use digital twins and complementary simulation methods to compare plausible scenarios, characterize uncertainty, and support model-informed decision-making.
- Work closely with statisticians and pharmacometricians so analyses complement established trial-design methods and align with clinical and regulatory objectives.
- Communicate assumptions, limitations, and decision-relevant insights clearly to technical and non-technical stakeholders.
- Analyze and integrate clinical trial data and, where relevant, biomarkers, imaging, digital health data, omics, patient-reported outcomes, and real-world data.
- Partner with data engineering and platform teams to create analysis-ready datasets, data-quality checks, lineage, and reproducible pipelines.
- Contribute to scientific documentation, presentations, publications, and responsible adoption of digital-twin methods across clinical development.
- Define the intended use, decision context, success criteria, and limitations of each model before development.
- Ensure models are interpretable, auditable, reproducible, and supported by evidence appropriate to their intended use.
- Apply appropriate governance, privacy, security, and scientific-review requirements for clinical data and analytical outputs.
- Clearly distinguish exploratory analyses from validated decision-support evidence and avoid overclaiming model capability.
Requirements
- PhD in data science, statistics, biostatistics, pharmacometrics, computer science, biomedical informatics, engineering, mathematics, operations research, or a related quantitative field.
- Demonstrated experience developing digital twins, virtual patients, disease-progression models, clinical trial simulations, synthetic controls, or closely related computational models.
- Strong experience applying machine learning, statistical modeling, causal inference, or simulation to biomedical, healthcare, pharmaceutical, or other complex scientific data.
- Strong programming skills in Python, R, or a comparable analytical language, with experience using version-controlled and reproducible workflows.
- Sufficient clinical-development knowledge to frame relevant questions and collaborate effectively with clinical and quantitative trial experts.
- Ability and interest to deepen clinical trial-design knowledge through partnership with clinical scientists, biostatisticians, and pharmacometricians.
- Strong written and verbal communication skills and demonstrated effectiveness in cross-functional scientific collaboration.
- Experience supporting clinical trial design, protocol development, clinical development strategy, model-informed drug development, or regulatory-facing quantitative evidence.
- Familiarity with one or more trial-design topics, such as endpoint strategy, sample size or power assumptions, patient selection or enrichment, longitudinal outcomes, missing data, adaptive designs, or decision criteria.
- Knowledge of digital-twin architecture and validation, supported by expertise in causal inference, Bayesian modeling, survival analysis, longitudinal modeling, clinical trial simulation, or synthetic controls.
- Experience with clinical data standards and common clinical-development data sources.
- Experience with biomarker, imaging, omics, digital health, or real-world data and integration of multiple data modalities.
- Experience with scalable cloud-based analytical environments.
- Track record of translating analytical work into decisions, reusable capabilities, publications, presentations, or other measurable scientific impact.
Skills
- Digital twins
- Simulation
- AI/ML
- Clinical development
- Quantitative strategies
- Interpretable analyses
- Protocol design
- Patient selection
- Endpoint strategy
- Dosing
- Operational planning
- Decision-making under uncertainty
- Disease trajectories
- Treatment response
- Clinical trials
- Mechanistic modeling
- Statistical modeling
- Causal inference
- Machine learning
- Validation
- Robustness
- Transportability
- Uncertainty quantification
- Reproducible workflows
- Reusable components
- Target populations
- Eligibility criteria
- Enrichment strategies
- Comparators
- Visit schedules
- Dosing strategies
- Operational tradeoffs
- Model-informed decision-making
- Biomarkers
- Imaging data
- Digital health data
- Omics data
- Patient-reported outcomes
- Real-world data
- Data integration
- Data engineering
- Data quality checks
- Data lineage
- Reproducible pipelines
- Scientific documentation
- Scientific presentations
- Scientific publications
- Model interpretability
- Model auditability
- Model reproducibility
- Governance
- Privacy
- Security
- Scientific review
- Exploratory analysis
- Decision-support evidence
- Python
- R
- Version control
- Cross-functional collaboration
- Model-informed drug development
- Regulatory-facing quantitative evidence
- Endpoint strategy
- Sample size
- Power assumptions
- Longitudinal outcomes
- Missing data
- Adaptive designs
- Decision criteria
- Digital-twin architecture
- Bayesian modeling
- Survival analysis
- Longitudinal modeling
- Synthetic controls
- Clinical data standards
- Cloud-based analytical environments
Location
- Indianapolis, IN Metro
- Remote (EST Candidates)
Work Type
- Hybrid
- Contractor Full-Time
Experience Level
- Senior
Education Level
- PhD
About the Company
- Xenon7 works with leading enterprise clients and innovative startups on high-impact projects across Data, AI, Cloud, and Software Engineering.
- Xenon7's expertise in AI solution architecture and specialized technical talent allows them to partner with enterprise leaders on transformative initiatives, driving innovation and business growth.