Principal Scientist at Clinical Dynamix, Inc.

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