Principal Statistician, Exploratory Statistics
Oncology is a key therapeutic area for AbbVie, with a portfolio consisting of three marketed products — Imbruvica, Venclexta, and Empliciti — and a pipeline containing multiple promising new molecules that are being studied in nearly 200 clinical trials in 19 different types of cancer.
AbbVie is expanding its oncology hub on the West Coast, with three sites in the San Francisco Bay Area (Redwood City, South San Francisco, and Sunnyvale) focused on the discovery and development of novel oncology therapies. More than 1,000 AbbVie scientists, clinicians, and product developers with strong entrepreneurial roots work across these three sites. They combine their expertise in immuno-oncology, stem cells, and cell-signaling with their knowledge of bispecific antibodies, antibody-drug conjugates (ADCs), and covalent-inhibitor technologies to discover and develop novel cancer treatments. This position will be based at AbbVie's location in either Redwood City, CA or South San Francisco, CA.
Exploratory Statistics 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.
We have an exciting opportunity for a senior level statistician (level and position title commensurate with experience) reporting to Director, Statistics.
- Help build statistical capabilities in drug discovery & exploratory clinical/translational research by providing strategic input and leadership to analyze large and complex data sets derived from patient samples and pre-clinical models of disease. Objectives may be to support novel target identification, identifying markers of disease progression and treatment response and for patient selection or stratification in clinical trials. Sources of data will likely include Genomics (high throughput gene expression arrays, CGH arrays, next generation sequencing, microRNA, etc.), Proteomics, Imaging, and flow based cytometric assays, along with the clinical and pre-clinical pharmacology data.
- Develop and maintain good working relationships with discovery and clinical scientists, statisticians, computational biologists, and external collaborators to drive program decisions as part of a multidisciplinary team.
- Collaborate with external colleagues on consortia and other research projects relevant to biomarker discovery and evaluations.
- 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.
- 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.
- Proactively propose opportunities for productivity improvements and implementation plans.
- Mentor junior staff, proactively help with both their technical and career development, and seek general feedback and technical input from colleagues.
- At least 6-8 years of post-doctoral related experience with demonstrated skills/accomplishments. Grade level and title will be commensurate with experience and expertise.
- Ph.D. in biostatistics, with some coursework/experience in bioinformatics, biochemistry, molecular biology, genetics, and related subjects.
- 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.
- Relevant academic/industry experience on topics related to drug discovery, clinical genomics and other applications mentioned above.
- Strong programming and computing skills.
- Excellent communication, presentations and report writing skills, and the ability to explain complex technical details in simple language.
Key Leadership Competencies:
- Builds strong relationships with peers and cross functionally with partners outside of team to enable higher performance.
- Learns fast, grasps the 'essence' and can change the course quickly where indicated
- Raises the bar and is never satisfied with the status quo
- Creates a learning environment, open to suggestions and experimentation for improvement
- Embraces the ideas of others, nurtures innovation and manages innovation to reality
Equal Opportunity Employer Minorities/Women/Veterans/Disabled