Data Scientist Jobs

About The Position

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.

Nice To Haves

• Experience supporting clinical trial design, protocol development, clinical development strategy, model-informed drug development, or regulatory-facing quantitative evidence is strongly preferred.
• 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, 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.

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 digital-twin use cases and contexts of use with clinical and quantitative experts so each model 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 applicable 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 with statisticians and pharmacometricians to complement established trial-design methods and align analyses 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.
• Distinguish exploratory analyses from validated decision-support evidence and avoid overclaiming model capability.