Senior Statistical Methodology Data Scientist

Role overview

Responsibilities

• check_circle Provide expert methodological guidance to study teams on complex study design, analysis strategies and quantitative decision-making across programs.
• check_circle Lead the development and implementation of innovative statistical approaches, such as estimand frameworks, trial simulations, covariate adjustment strategies, model-based projections and quantitative go/no-go frameworks.
• check_circle Translate methodological insights into scalable solutions, including templates, training, open-source tools and best practices that strengthen analytical excellence across the organization.
• check_circle Support portfolio-level analyses, scenario modeling and quantitative frameworks that inform clinical strategy, optimize development decisions and enable evidence-based prioritization.
• check_circle Represent Roche externally through scientific collaborations, consortia, working groups, publications or regulatory-facing initiatives, helping the organization stay at the forefront of methodological advances.

Basic qualifications

• You hold a PhD, in Statistics, Data Science, Computer Science or another related quantitative discipline.
• You have 2+ years of experience designing and applying advanced statistical or data science methods to complex biomedical data, ideally in clinical trial research or pharmaceutical development.
• You bring strong expertise in statistical computing, advanced modeling and the development of novel or fit-for-purpose analytical solutions for complex datasets.
• You are recognized as a technical expert and trusted thought partner, with the ability to translate complex methodology into clear recommendations for scientific, clinical and cross-functional stakeholders.
• You have experience contributing to internal standards, capability building, training or strategic initiatives, and you enjoy mentoring others to strengthen methodological understanding and adoption.
• You bring a collaborative and inclusive mindset, show respect for cultural differences in a global environment, and ideally have a track record of publications, presentations or thought leadership in emerging areas such as AI/ML, real-world data integration or predictive biomarkers.