Principal Scientist (Contract to Perm); Hybrid in office 3 days per week

Description

Reporting to the VP, Clinical Pharmacology, the Principal Scientist will work with Discovery Research and Development team members, to support PPK/PKPD/ER dataset preparation and exploratory data visualization, across early and late-stage programs.

This role may be based in our Waltham, MA headquarters, which would be a hybrid role, or it can also be fully remote. Our office-based employees are required to work in the office three (3) days a week. If remote, travel to headquarters for meetings would be required at the discretion of management.

This role is a contract-to-hire position.

Responsibilities (including, but not limited to):

•       Clean PK/Immunogenecity related datasets
•       Perform PK database lock
•       Transform the relevant STDM and ADaM datasets, and other sources of data to enable PPK, PKPD and ER analyses
•       Develop SAS (or R) codes to perform data visualization, descriptive statistical summary in TLFs
•       Ensure dataset formatted according to CDISC standard  
•       Collaborate with biostatistics programmers, statisticians, and pharmacometricians, to ensure data consistency and accuracy for general PK, PK/PD and ER analyses
•       Stay informed with the emerging literature in programming approaches in PMx field
•       Maintain current understanding of global regulatory expectations for CDISC requirements

Requirements

•       Requires an MS or PhD or equivalent in educational background in Statistics, Mathematics, Informatics, Chemical engineer, Computer Science, Pharmaceutical Science, or a related field with 10+ (MS degree) or 5+ (PhD) years of clinical pharmacology related programming or data science experience
•       Seasoned in SAS programming (or R)
•       Experienced with CDISC (eg, STDM, ADaM) standards, and experienced with Clinical Pharmacology related datasets (eg, PC, adpc, adpp, and PPK/ER datasets)
•       Demonstrated ability to handle uncleaned data, identify data issues, and implement different programming procedures according to the pharmacometrics modelling needs
•       Excellent attention to fine data details