Basic Function and Scope of the Position:
The Biostatistician will act as a lead analyst working on data related to the development of clinical assays in a CLIA/CAP laboratory. The successful candidate will possess strong statistical and analytic skills, solid programming experience, and a high level of professional maturity.
Tasks and Responsibilities:
- Provide statistical guidance and perform analyses that enhance Applied Proteomics’ multiple-analyte clinical assays. Develop a deep understanding of Applied Proteomics’ tests. Serve as a key computational resource to propose and evaluate methods in the Product Research and Development laboratory.
- Design and analyze development, verification, validation, and other studies using DOE approaches.
- Design and execute analyses to assess experimental factor effects and quality-related measures in the laboratory. Perform ongoing monitoring of assay measures and outcomes. Proactively explore the data to uncover elusive problems and ensure high-quality tests.
- Perform other statistical analyses as needed. This may include machine learning to build classifiers for clinical tests.
- Develop software to perform these analyses. Contribute to documentation and testing of production‐level computational software.
- Clearly summarize technical findings in oral, written, and graphic forms for internal and external scientific colleagues.
- Collaborate with a team of laboratory scientists, bioinformaticians, regulatory affairs specialists, and quality assurance specialists.
- MS or Ph.D in Statistics, Bioinformatics, Computational Biology, or related field.
- 2+ years of industry experience preferred. Equivalent post‐doctoral experience will be considered.
- Fluency in scripting languages (e.g. Python, Perl) and statistical software packages (e.g. R) is required.
- Previous experience working with diagnostic or prognostic tests (lab tests or IVDs) under CLIA, CAP, or FDA guidelines and regulations is highly desirable.
- Experience with experimental design, and with statistical approaches to build and assess clinical assays and clinical performance, is highly desirable.
- Experience in developing software for computational analyses of multiplexed assay data is desirable.