Clinical Digital Twin Scientist—AI/ML & Trial Simulation

Workana LLC; Indianapolis (IN); Hybrid; USD 100,000 - 180,000; Part time

Benefits offered by this job

Competitive salary with travel

Hybrid/Remote flexibility

Career growth

Independent contractor

Job summary

Workana is seeking a Clinical Data Scientist to lead the development and evaluation of digital twins for patient trajectories, disease progression, and trial outcomes. You will blend mechanistic, statistical, causal, and ML approaches to build reusable analytical workflows and inform design decisions.

Collaborating with biostatisticians, pharmacometricians, and data engineers, you will ensure data integration across trials, biomarkers, real-world data, imaging, and omics, while maintaining

Qualifications

• PhD in a quantitative field related to data science, statistics or biomedical informatics.
• Experience building digital twins, virtual patients, disease progression models, or trial simulations.
• Strong programming skills in Python or R with reproducible workflows.
• Experience applying ML, statistics, causal inference to healthcare or biomedical data.

Responsibilities

• Lead design, development, calibration, and validation of digital twins for patients and trials.
• Combine ML, statistical modeling, causal inference, and mechanistic approaches.
• Collaborate with trial teams to evaluate design options such as populations and endpoints.
• Integrate multi-modal data including trials, biomarkers, real-world data, imaging, and omics.
• Ensure model quality, interpretability, and clear communication to stakeholders.

Skills

ML & statistics

Causal inference

Simulation

Python/R

Clinical development knowledge

Problem solving

Education

PhD in Data Science/Statistics/Biostatistics/Pharmacometrics/CS/Bioinformatics

Tools

Git

Job description

Workana is seeking a Clinical Data Scientist to lead the development and evaluation of digital twins for patient trajectories, disease progression, and trial outcomes. You will blend mechanistic, statistical, causal, and ML approaches to build reusable analytical workflows and inform design decisions.

Collaborating with biostatisticians, pharmacometricians, and data engineers, you will ensure data integration across trials, biomarkers, real-world data, imaging, and omics, while maintaining