Principal Scientist, Stat. Programming- Early Oncology Development (Hybrid)

Primary Activities

• Provide technical consultation and analytical support to early development statisticians for exploratory and unplanned analyses
• Address operational challenges requiring strategic solutions across early oncology development
• Support development of programming standards to enable efficient, high-quality programming deliverables
• Facilitate global adoption of standards and programming best practices within early oncology
• Pre-process, filter, and manipulate complex data for molecular biomarker, imaging, and other external (non-traditional lab) data
• Lead and actively contribute to departmental strategic initiative project teams

Education and Minimum Requirement

• BA/BS in Computer Science, Statistics, Applied Mathematics, Life Sciences, Engineering or related field + 12+ years SAS/R programming in a clinical trial environment; or
• MS in Computer Science, Statistics, Applied Mathematics, Life Sciences, Engineering or related field + 10+ years SAS/R programming in a clinical trial environment

Department/Position Required Skills and Experience

• Excellent interpersonal, written/oral/presentation skills; strong project management and leadership across multiple complex programs
• Significant expertise in SAS and clinical trial programming (data steps, procedures, SAS/MACRO, SAS/GRAPH)
• Experience setting strategy and developing statistical programming deliverables (datasets, analyses, report tables/graphics) meeting quality, compliance, and timeliness requirements
• Ability to design complex algorithms; implement statistical methods not available in commercial software
• Experience with large databases; CDISC and ADaM
• Quality/process compliance; strategic thinking; anticipate stakeholder requirements; work across cultures/geographies

Preferred Skills

• Early oncology development; developing/implementing programming standards; SOPs/SDLC; Microsoft Project; regulatory submission leadership; PK/PD, process improvement methodologies; Python and R