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Xenon Seven
Xenon Seven
11 – 50 Employees
ConsultingHealthcareManufacturing
Xenon Seven provides AI and data consulting to organizations across healthcare, financial services, retail, technology, automotive, energy, and manufacturing. Its work spans generative AI, predictive analytics, computer vision, robotics, and decision support, helping teams assess opportunities, build and scale AI capabilities, and improve the infrastructure behind them. The company draws on a network of AI specialists from more than 20 institutions and emphasizes secure implementation and reliable transitions into AI-enabled operations.
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Clinical Data Scientist, Digital Twins and Clinical Trials

Builds digital twins and simulations to inform pharmaceutical clinical-trial design. Integrates clinical data with AI and machine learning to support evidence-based development decisions.

USUnited StatesILIllinoisOHOhioHybridFull-timeMiddleSeniorEnglish RequiredData Scientist
CloudPython

Description

  • Lead the development and evaluation of patient, disease, treatment-response, and clinical-trial digital twins using clinical-trial and relevant external data.
  • Apply mechanistic, statistical, causal, and machine-learning methods to create digital twins for alternative populations, interventions, dosing strategies, endpoints, and trial designs.
  • Set use cases and define how digital twins will be used with clinical and quantitative experts.
  • Evaluate model calibration, validation, robustness, transportability, uncertainty, and practical relevance using internal and external strategies.
  • Build reproducible workflows and reusable digital-twin components across programs and therapeutic areas.
  • Work with trial teams to assess target populations, eligibility criteria, enrichment strategies, comparators, endpoints, visit schedules, dosing strategies, and operational tradeoffs.
  • Use digital twins and simulations to compare scenarios, quantify uncertainty, and inform model-based decisions.
  • Coordinate with statisticians and pharmacometricians to align analyses with clinical and regulatory goals.
  • Explain assumptions, limitations, and decision-relevant findings to technical and non-technical stakeholders.
  • Analyze and integrate clinical-trial data, biomarkers, imaging, digital health data, omics, patient-reported outcomes, and real-world data.
  • Partner with data engineering and platform teams to prepare analysis-ready datasets and establish data-quality checks, lineage, and reproducible pipelines.
  • Prepare scientific documentation, presentations, and publications, and support responsible adoption of digital-twin methods.
  • Set intended use, decision context, success criteria, and limitations before developing models.
  • Ensure models are interpretable, auditable, reproducible, and supported by evidence.
  • Follow governance, privacy, security, and scientific-review requirements for clinical data and analytical outputs.
  • Separate exploratory analyses from validated decision-support evidence and accurately represent model capabilities.

Requirements

  • PhD in data science, statistics, biostatistics, pharmacometrics, computer science, biomedical informatics, engineering, mathematics, operations research, or a related quantitative discipline.
  • Demonstrated experience building digital twins, virtual patients, disease-progression models, clinical-trial simulations, synthetic controls, or similar computational models.
  • Strong background 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, and experience with version control and reproducible workflows.
  • Enough knowledge of clinical development to frame relevant questions and work effectively with clinical and quantitative trial experts.
  • Ability and interest in expanding clinical-trial design knowledge alongside clinical scientists, biostatisticians, and pharmacometricians.
  • Strong written and verbal communication, with a record of effective cross-functional scientific collaboration.
  • Preferred: experience in clinical-trial design support, protocol development, clinical-development strategy, model-informed drug development, or regulatory-facing quantitative evidence.
  • Familiarity with 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, 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, including integration across multiple data types.
  • Experience using scalable, cloud-based analytical environments.
  • A record of translating analytical work into decisions, reusable capabilities, publications, presentations, or other measurable scientific outcomes.

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