As required.The Principal Biostatistician is responsible for all statistical work, scientific and operational, for one or more assigned trials in collaboration with the clinical trial team. May support project level deliverables for a development project with supervision. Leads implementation of modern and innovative clinical trial/experimental designs, statistical models, analyses and data exploration at the study level.
Contribute to drug development efforts of the Biomarker and Diagnostics Biometrics team by providing high-quality and timely statistics and bioinformatics expertise in the areas of Biomarkers and Diagnostics (B&D) data analysis, integration and interpretation. Provide statistical and data-analytic support for the design and subsequent analyses of molecular biomarker and clinical data from clinical trials. Provide timely and high quality input in to all Biostatistics deliverables, e.g. protocol, study set-up, analysis plan, analysis reports and publications. Work with different diverse modalities of complex and high dimensional data such as RNA-Seq, Microarray gene expression, Next Generation Sequencing. Use statistical software packages (e.g. SAS, R, Bioconductor) or similar data analysis software and high-level programming languages (C++ etc.). Write functions and packages in different programming environment. Write statistical analysis plans of biomarker analysis for clinical trials and data analysis section of publications. Applied statistical techniques; Bayesian hierarchical models, generalized linear models, linear mixed model, generalized linear mixed models, data-mining (PCA, clustering, tree-based model), high dimension variable selection (Lasso, Elastic net), predictive modeling, multiplicity correction techniques, and gene set enrichment analysis, clustering and data mining techniques. Identify and follow adequate B&SR (biostatistics and statistical reporting) processes according to projects specific needs (exploratory setting vs. regulatory setting). Work with heterogeneous and cross functional global teams of correlative/precision medicine scientists, biologists, clinical and regulatory experts. Ensure timeliness and high quality contribution to all Biostatistics' deliverables, e.g. protocol, study set-up, and analysis plan, analysis reports, regulatory submissions and publications for the assigned clinical projects. Communicate with clinical development and regulatory teams, as well as external partners through reports and presentations. Promote the use of/develop suitable data analysis tools across platforms using appropriate programming languages. Present data analysis results, conclusions and future plans and explain statistical design to project team members and scientists.
The Novartis Group of Companies are Equal Opportunity Employers and take pride in maintaining a diverse environment. We do not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status.
-PhD degree in Statistics, Mathematics, or Biostatistics and 6 months research or industry experience in statistical support English.
-Must have experience: in statistical design, analyses of genomic and proteomic data; providing statistical, bioinformatics and data-analytic support for the biomarker data; applying advanced statistical techniques: Bayesian hierarchical models, generalized linear models, generalized linear mixed models, data-mining techniques(PCA, clustering, tree-based model), high dimension variable selection (Lasso, Elastic net), multiplicity correction techniques and gene set enrichment analysis (GSEA); with high-dimensional biomedical data such as SomaScan, RNA-Seq, Microarray gene expression, Next generation sequencing biomarker data; in performing statistical analysis using statistical software packages (e.g. SAS, R, Bioconductor) and high-level programming languages C++ etc.; experience with writing functions and packages; communicating with scientists/non-statisticians to explain statistical designs/methods and presenting data analysis results; writing statistical analysis plans and data analysis sections of publications.
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