Principal RWE Biostatistician

This is a fully remote position, open to applicants in United Kingdom.

📋 Description

• Lead and facilitate advanced real-world evidence (RWE) analyses utilizing extensive real-world data to guide clinical development, regulatory strategies, health economics and outcomes research (HEOR), and payer decisions.

• Evaluate statistical analysis plans and contribute insights for observational studies and target trial emulations through electronic health records (EHRs), claims, registries, genomics, and digital health data.

• Employ causal inference and statistical learning techniques, such as propensity scores, inverse probability weighting, survival and longitudinal models, and representation learning, to tackle confounding, bias, and missing data issues.

• Design and execute machine learning workflows for prediction, patient stratification, phenotyping, and natural language processing (NLP) using both structured and unstructured healthcare data.

• Integrate outputs from AI/ML with classic statistical methods to enhance interpretability, scientific rigor, and compliance with regulatory standards.

• Create scalable, reproducible analytical processes in Python and/or R by applying software engineering principles, thorough documentation, and quality-control best practices.

• Collaborate with clinical scientists, epidemiologists, data engineers, and HEOR stakeholders to establish tailored analytical approaches.

• Convert intricate quantitative results into actionable insights for both technical and non-technical audiences.

• Assist in governance preparations, regulatory submissions, audits, and the dissemination of scientific knowledge.

• Produce high-quality analytical outputs within agreed timelines in a global, matrixed functional service provider (FSP) environment.

⛳️ Requirements

• PhD in Statistics, Applied Mathematics, Data Science, or a related quantitative discipline, or a Master’s degree with 3-5+ years of pertinent industry experience.

• Strong foundation in mathematical statistics, probability theory, and statistical modeling.

• Proven experience in applying machine learning or AI techniques to large or high-dimensional datasets.

• Proficiency in statistical programming languages such as SAS, R, and Python.

• Familiarity with machine learning frameworks like scikit-learn, PyTorch, and TensorFlow is preferred.

• Comprehensive knowledge of causal inference and the design of observational studies.

• Exceptional communication skills with the ability to work effectively in cross-functional, global teams.

• Experience with large-scale real-world data, encompassing administrative claims, EHRs, 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 techniques.

• Publications or applied research in machine learning within the context of healthcare or real-world evidence.

• Enthusiasm for translating academic machine learning into practical, regulatory-grade analytics.

🏝️ Benefits

• Ongoing training, development, and support.

• Significant opportunities for professional growth.

• Autonomy and ownership while working within a sponsor-dedicated program.