Principal Biostatistician FSP - RWE and Medical Affairs

The role is responsible for statistical activities in support of Medical Affairs and analyzing RWD, including post approval value evidence generation, secondary publications, post marketing study design and study protocol development, analysis and reporting of observational or clinical study data, and other post hoc and exploratory analyses as needed. 

Summary of Key Responsibilities 

• Leads statistical support for post approval value evidence generation and reimbursement submissions
• Conducts post hoc analysis to support publications and presentations 
• Reviews and authors abstract, manuscript, regulatory documents 
• Collaborates with cross-functional team to support observational studies 
• Develops statistical sections of study protocols and statistical analysis plans. 
• Collaborates with Data Management and Medical Research on design of eCRFs
• Provides statistical guidance on conduct of ongoing studies. 
• Accountable for the collaboration with Statistical Programming to implement statistical analysis of all clinical trial, registry, observational and non-interventional data supporting Medical Affairs needs
• Contributes to observational study reports and regulatory documents, e.g., DSURs, briefing documents, etc. 
• Contributes to scientific articles, summarizing data collected in Alnylam studies. 
• Participates in other activities and meetings to support Biostatistics and the Medical Affairs team as needed

Qualifications 

• PhD in Biostatistics, Statistics, or equivalent with at least 3 years pharmaceutical biostatistics experience; or MS with at least 5 years’ relevant experience 
• Excellent written and oral communication and presentation skills
• Strong knowledge of research methodology in HEOR (Health Economics and Outcomes Research) and pharmacoepidemiology
• Ability to explain methodology and the consequences of decisions in lay terms
• Significant, independent experience with statistical modeling of time-to-event outcomes (e.g., death, AE, hospitalization) including repeated or recurrent data and competing risk models
• Advanced analytics techniques such as propensity score matching in analyzing RWD
• Experience in survival analysis
• Proficiency in SAS statistical procedures (e.g., PHREG, GENMOD, LOGISTIC, LIFETEST, MIXED)
• Extensive experience programming in SAS
• Extensive experiences working on data analysis with clinical studies database  
• Experience working on post-approval observational and clinical studies
• Understanding of ICH GCP as well as general knowledge of industry practices and standards
• Experiences in R programming language and other statistical software. 
• Experience with CDISC, including SDTM, ADaM, CDASH