Clinical Data Scientist - Digital Twins & AI/ML

Workana

1d•Remote

About The Position

Workana is recruiting for an innovative clinical development organization that uses digital twins, AI/ML, and computational modeling to turn biological and clinical-trial data into quantitative strategies. The Clinical Data Scientist will join a multidisciplinary team of physicians, biostatisticians, pharmacometricians, and data engineers to support clinical-trial design and development decisions. The role leads the development, evaluation, and application of digital twins to simulate virtual patients, disease trajectories, treatment responses, and clinical trials. It combines mechanistic, statistical, causal, and machine-learning methods to create fit-for-purpose models and reproducible analytical workflows. The scientist will support decisions involving protocol design, patient selection, enrichment, dosing, endpoints, and operational planning, and communicate model assumptions, uncertainty, and decision-relevant insights to technical and non-technical stakeholders. The posting describes a fully remote opportunity for professionals based in Latin America, working with an international English-speaking team. It also lists flexible arrangements as hybrid in Indianapolis, Indiana, or fully remote within the U.S. East Coast with occasional travel. The position is offered under a long-term independent contractor agreement. The selection process usually has four stages and takes an average of four weeks, potentially including additional technical rounds or take-home tests.

Requirements

PhD in Data Science, Statistics, Biostatistics, Pharmacometrics, Computer Science, Biomedical Informatics, or a related quantitative field.
Demonstrated experience developing digital twins, virtual patients, disease-progression models, or clinical-trial simulations.
Strong experience applying machine learning, statistical modeling, causal inference, or simulation to healthcare, pharmaceutical, or complex biomedical data.
Advanced programming skills in Python or R, with experience using version-controlled and reproducible workflows.
Sufficient clinical-development knowledge to frame quantitative research questions and collaborate effectively with trial experts.
Strong problem-solving skills and ability to translate complex analytical results into actionable clinical-development decisions.

Nice To Haves

Direct experience supporting clinical-trial design, protocol development, model-informed drug development (MIDD), or regulatory-facing quantitative evidence.
Expertise in Bayesian modeling, survival analysis, longitudinal modeling, adaptive trial design, or synthetic control arms.
Experience working with scalable cloud-based analytical environments such as AWS, GCP, or Azure.
Experience working with clinical data standards and multimodal biomedical data integration.

Responsibilities

Lead the design, development, calibration, and validation of digital twins for patients, disease progression, and trial outcomes using clinical-trial and external data.
Combine machine learning, statistical modeling, causal inference, and mechanistic approaches to build fit-for-purpose computational models.
Partner with trial teams to evaluate protocol-design options, including target populations, eligibility, enrichment strategies, endpoints, and dosing.
Analyze and integrate multimodal datasets, including clinical trials, biomarkers, real-world data, digital health, imaging, and omics.
Collaborate with data engineering and platform teams to build reproducible pipelines, reusable digital-twin components, and analysis-ready datasets.
Work with biostatisticians and pharmacometricians to ensure digital-twin outputs complement established trial-design methods.
Ensure model quality, interpretability, auditability, and responsible use by defining decision contexts, success criteria, and limitations.
Communicate model assumptions, uncertainty, and decision-relevant insights to technical and non-technical stakeholders.