Discovery and Early Pipeline Statistics (DIVES) is part of the Data and Statistical Sciences (DSS) organization in AbbVie R&D. This group provides statistical expertise globally for various groups in drug discovery, development sciences and for biomarker & genomics studies in early to late-stage clinical trials. Examples of applications/topics supported include in-vitro screening, in-vivo pharmacology, genomics (high-throughput mRNA expression arrays, CGH arrays, next generation sequencing, microRNA, genotype data, etc.), proteomics, imaging, and other biomarker data generated from pre-clinical and clinical studies, research and GLP assays used for measuring biomarkers, pharmacokinetics and immunogenicity response in preclinical and clinical studies and ADMET screening assays. Key Responsibilities:
- Statistical collaborations and consultations on some or all of the following applications: Target Identification ,Personalized medicine, Subgroup identification, In-Vitro and In-Vivo pharmacology assays, Genomics (CRISPR/Cas9 functional genomic screen, high throughput gene expression arrays, CGH arrays, next generation sequencing, microRNA, etc.), Proteomics, Imaging, and other biomarker data generated from pre-clinical and clinical studies, Design of biomarker-based clinical trials for personalized therapies, and general methodological research on related topics.
- Development of novel functional genomic screening hit selection algorithms. Integration of CRISPR screening data with other genomics data (expression profiling, genotype, etc.). Perform Gene network/pathway analysis and visualization
- Develop and maintain good working relationships with statisticians, clinical and discovery scientists, and personnel from other areas within the company and also with scientists from external institutions when communicating/collaborating on various projects.
- Maintain and develop further expertise in the use of R, SAS, UNIX utilities, JAVA, Perl, JMP, VB and other programs, and continue development of such utilities/macros to improve process flow, as well as maintaining appropriate documentation.
- Consult/collaborate with other statisticians on various statistical methodologies and statistical computing applications. Evaluation and implementation of advanced statistical models. Proactively seek input and review from other experts within and outside the group on various projects and research activities, and share technical information when appropriate.
- Propose opportunities for productivity improvements and implementation plans.
- Ph.D. in Statistics, Biostatistics, or a highly related field, with some applied consulting or research experience on topics related to pharmaceutical research, bioinformatics, target identification, biomarkers, and subgroup identification.
- 1-3 years of related experience with demonstrated skills/accomplishments.
- Expertise in statistical methodologies such as predictive modeling, machine learning methods, nonlinear regression, mixed effects models, multivariate analysis, robust statistics, adaptive designs, etc.
- Familiarity with NGS-based pooled CRISPR screening data
- Relevant academic/industry experience on topics related to bioinformatics, genomics, clinical biomarker studies, personalized medicine, and other applications mentioned above.
- Strong computing skills in R, SAS and other programming languages.
- Good grasp of clinical, biochemistry, molecular biology, and related subjects.