Jobs at Cytel

Designs, develops, and deploys secure, scalable AI solutions for Statistical Programming workflows. Builds Generative AI, RAG, and agentic applications involving ingestion, retrieval, code generation, validation, and human review. Develops reusable services and APIs while implementing traceability, reproducibility, evaluation, regression testing, monitoring, and LLMOps practices. Collaborates with programming, architecture, IT, security, validation, and governance teams to move AI proofs of concept into production, regulated environments.

Provides expert leadership and oversight of CDISC standards across clinical studies and regulatory submissions. Reviews SDTM and ADaM datasets, annotated CRFs, define.xml files, Reviewer's Guides, and submission packages for compliance, traceability, and quality. Advises cross-functional teams and CROs, supports standards governance, develops documentation and training, mentors programmers, and improves global programming processes and submission readiness.

Lead statistical activities for Phase II–III clinical trials from protocol design through reporting. Responsibilities include SAP development, sample size calculations, statistical analysis, TLF review, database lock and submission support, regulatory responses, vendor oversight, and cross-functional collaboration. Use SAS and/or R for analyses, modeling, simulation, exploratory work, and quality control. Coordinate statistical activities across studies and apply adaptive, group sequential, count-data, machine learning, and deep learning methods.

Supports epidemiology and real-world evidence studies by preparing claims and EHR data, developing analytical databases, programming statistical analyses, creating algorithms, linking datasets, and producing tables, figures, reports, and presentations. Partners with epidemiologists, statisticians, vendors, clients, and internal stakeholders across Medical Affairs, Patient Safety, Risk Management, and Clinical Development. Requires expertise in SQL, SAS or R, RWE data sources, applied statistics, and project management.

Leads portfolio strategy, solution architecture, commercial growth, and delivery alignment for complex life sciences services. Partners with business development, marketing, sales, and functional experts to shape opportunities, design scalable offerings, support proposals, and improve profitability. Oversees portfolio governance, client outcomes, forecasting, scope management, and sales-to-delivery handoffs. Represents the organization with strategic clients and through industry thought leadership.

Provides technical and functional leadership for epidemiological and real-world evidence statistical programming. Leads hands-on development and validation of SAS and R programs, analysis datasets, tables, figures, and reproducible workflows using healthcare data. Mentors programmers, oversees internal and CRO deliverables, establishes standards and quality processes, supports regulatory and scientific outputs, and collaborates with epidemiologists, statisticians, and data scientists across projects and therapeutic areas.

Supports epidemiology and real-world evidence studies by preparing claims and EHR data, developing analytical databases, conducting statistical analyses, and producing tables, figures, reports, and presentations. The role assists with protocols and analysis plans, coordinates with data vendors and stakeholders, applies regression and survival methods, and supports Medical Affairs, Patient Safety, Risk Management, and Clinical Development initiatives. Strong SQL, SAS, or R expertise and experience with pharma RWE analytics are required.

Lead or support Phase I-IV clinical trial programming using SAS and CDISC standards to generate and validate SDTM/ADaM datasets, TLFs, and submission documents. Perform production and QC/validation programming, complex ad-hoc reports, and efficacy analyses while coordinating with cross-functional teams and serving as study lead when required.

Lead statistical design and analysis for Phase I-IV clinical studies, develop protocols and SAPs, perform efficacy and exploratory analyses, run simulations, apply advanced methods (mixed models, longitudinal), oversee datasets (ADaM/CDISC), use R and SAS for analyses, and provide strategic statistical guidance to cross-functional teams supporting regulatory submissions.