Clinical Statistician (Remote)

CLINICAL STATISTICIAN (REMOTE) REDWOOD CITY, CA Remote. Project Description:

• This group provides statistical expertise globally for various groups in precision medicine, drug discovery and development sciences for biomarker and exploratory studies in preclinical and early to late-stage clinical trials.
• Examples of applications/topics supported include in-vitro screening, in-vivo pharmacology, genomics, proteomics, imaging, and other biomarker data generated from pre-clinical and clinical studies, research and GLP assays used for measuring biomarker response or drug response in preclinical and clinical studies.
• Provide statistical supports to cross-functional biomarker project teams.
• Analyze large and complex data sets derived from patient samples and pre-clinical models of disease.
• Objectives may be to support novel target identification, identify pharmacodynamic biomarkers and treatment response, as well as prognostic/predictive biomarkers for patient selection or stratification in clinical trials.
• Sources of data will likely include Genomics, Proteomics, Imaging, and flow cytometry assays, along with the clinical and pre-clinical pharmacology data.
• Maintain and expand expertise in various computing tools to leverage internal and external data sets to drive decisions.
• Examples of such tools include R/Bioconductor, Spotfire, SAS, UNIX utilities, JAVA, Perl, Python, etc.
• Continue development of various analysis tools to improve the process.
• Collaborate with internal and external colleagues on research projects relevant to biomarker discovery and evaluations.
• The incumbent will work with other statisticians in the DIVES, ABA group.
• The working environment is highly collaborative and fast paced.

• Master or Ph.D. in biostatistics, with some coursework/experience in bioinformatics, biochemistry, molecular biology, genetics, and related subjects.
• Strong programming and computing skills in R, Python, Perl, SAS, etc.
• Expertise in genetic, genomic and proteomics data analysis, including raw data processing and modeling of processed/normalized data, and familiarity with various technological platforms.
• Expertise in statistical methodologies such as predictive modeling and inference, machine learning methods, mixed effects models, multivariate analysis, etc.
• Excellent communication, presentations and report writing skills, and the ability to explain complex technical details in simple language.
• Relevant academic/industry experience on topics related to drug development, biomarker discovery and other applications mentioned above .

• Experience of Machine learning algorithms is highly preferred.
• Experience dealing with biomarker data, such as RNA sequencing data, proteomics data, is a plus.