Viridian Therapeutics
At Viridian, we are focused on developing best-in-class medicines for people living with autoimmune and rare diseases. Leveraging our team’s expertise in antibody discovery and engineering, we have created a robust pipeline of differentiated investigational therapeutic candidates for well-validated targets.
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
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
Qualifications
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
#J-18808-Ljbffr
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
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
Qualifications
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
#J-18808-Ljbffr