Principal RWE Biostatistician
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Job Description
Principal RWE Biostatistician leading causal inference, machine learning, and real-world evidence analytics for Cytel’s pharmaceutical clients. Delivering regulatory-grade insights from large-scale healthcare data.
Responsibilities:
- Lead and support advanced RWE analyses using large-scale real-world data to inform clinical development, regulatory strategy, HEOR, and payer decisions
- Review statistical analysis plans and provide input for observational studies and target trial emulations using EHRs, claims, registries, genomics, and digital health data
- Apply causal inference and statistical learning methods, including propensity scores, inverse probability weighting, survival and longitudinal models, and representation learning, to address confounding, bias, and missing data
- Develop and implement machine learning pipelines for prediction, patient stratification, phenotyping, and NLP using structured and unstructured healthcare data
- Combine AI/ML outputs with traditional statistical approaches to support interpretability, scientific rigor, and regulatory readiness
- Build scalable, reproducible analytical workflows in Python and/or R using software engineering, documentation, and quality-control best practices
- Collaborate with clinical scientists, epidemiologists, data engineers, and HEOR partners to define fit-for-purpose analytical strategies
- Translate complex quantitative findings into actionable insights for technical and non-technical stakeholders
- Support governance readiness, regulatory submissions, audits, and scientific dissemination
- Deliver high-quality analytical outputs on agreed timelines within a global, matrixed FSP environment
Requirements:
- PhD in Statistics, Applied Mathematics, Data Science, or related quantitative field, or MS with 3-5+ years of relevant industry experience
- Strong grounding in mathematical statistics, probability, and statistical modeling
- Demonstrated experience applying machine learning or AI methods to large or high dimensional datasets
- Proficiency in statistical programming languages including SAS, R and Python
- Familiarity with ML frameworks such as scikit-learn, PyTorch, and TensorFlow preferred
- Solid understanding of causal inference and observational study design
- Excellent communication skills and ability to collaborate effectively in cross-functional, global teams
- Experience with large-scale real-world data, including administrative claims, EHR, OMOP/CDM, and registries
- Familiarity with cloud or big-data environments including SQL, Spark, Databricks, AWS, Azure, and GCP
- Experience with Bayesian modeling or probabilistic machine learning
- Publications or applied research in ML plus healthcare or RWE
- Interest in translating academic ML into real-world, regulatory-grade analytics
Benefits:
- Consistent training, development and support
- Significant professional growth opportunities
- Autonomy and ownership while working within a sponsor-dedicated program
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