Clinical Data Scientist

About Xenon7

Where elite tech talent meets world-class opportunities! At Xenon7, we work with leading enterprise clients and innovative startups on high-impact projects across Data, AI, Cloud, and Software Engineering. Our expertise in AI solution architecture and specialized technical talent allows us to partner with enterprise leaders on transformative initiatives, driving innovation and business growth.

Role purpose

Lead the development and application of digital twins to improve clinical trial design and support evidence-based clinical development decisions. The role prioritizes digital-twin, simulation, and AI/ML expertise, supported by clinical-development experience and close partnership with trial-design experts.

Position overview

You will join a multidisciplinary clinical development environment and partner with clinical scientists, physicians, biostatisticians, pharmacometricians, biomarker experts, data scientists, data engineers, and other stakeholders. You will translate clinical development questions into quantitative strategies and deliver interpretable analyses that inform protocol design, patient selection, endpoint strategy, dosing, operational planning, and decision-making under uncertainty.

The role prioritizes digital twins as the primary capability for simulating patients, disease trajectories, treatment response, and clinical trials. The successful candidate will bring strong digital-twin, simulation, and AI/ML expertise, along with sufficient clinical-development knowledge to work effectively with trial teams. Deeper trial-design expertise can be developed through close collaboration with clinical scientists, biostatisticians, and pharmacometricians.

Key responsibilities

Digital-twin development and validation

• 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.

Clinical trial design and quantitative decision support

• 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.

Data integration and scientific collaboration

• 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.

Model quality and responsible use

• 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.

Preferred qualifications

• Strongly preferred: 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.