Job Description

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.

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

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