Manager / Senior Manager, Discovery and Exploratory Statistics

Redwood City, CA, US
Oct 13, 2019
Required Education
Masters Degree/MBA
Position Type
Full time
DIVES is part of the Data and Statistical Sciences (DSS) organization in AbbVie R&D. This group located on the West Coast provides statistical expertise for various groups in drug discovery, translational oncology, development sciences and clinical biomarkers in early stage oncology 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. We are growing our team and are currently looking for a Manager/Senior Manager, DIVES based at AbbVie's Redwood City, CA location.

In this position, you will work independently with discovery researchers, translational scientists, DMPK scientists and clinical biomarker leads in the design, collection, analysis and reporting of multi-dimensional biomarker data from Discovery to early stage clinical development to enable objective decision-making for each drug development program.
Key Responsibilities:
  • Collaborating with translational biomarker scientists and clinicians in the design, analysis and reporting of biomarker and assay studies in association with early phase immune-oncology clinical trials. This includes pharmacodynamic biomarker analysis, prognostic and predictive biomarker identification and development, bioassay and companion diagnostic test development, patient subgroup identification based on biomarkers and clinical variables.
  • Accountable for data integrity of the responsible statistical analysis and deliverables.
  • Providing expertise for the design, analysis and reporting of biomarker-based clinical trials or other scientific research studies. Independently developing biomarker section of clinical trial protocols or biomarker analysis plans. Implementing sound statistical methodology in scientific investigations.
  • Acting as a point of contact for DIVES to collaborate with scientists in discovery and translational science units in the design, analysis and reporting of in vitro, in vivo pharmacology studies, and human clinical trials. Identify markers and signatures from internal/external omics/imaging and other biomarker data for target identification, mechanism of action, resistant mechanism, disease progression, patient selection and stratification for precision medicine.
  • Independently perform statistical analyses as per the biomarker analysis plan. Independently identifying issues arising in the study design; conducting and proposing scientifically sound approaches. Evaluating appropriateness of available software for planned analyses and assessing needs for potential development of novel statistical methodology.
  • Fully accountable for statistics/data presentation and inference. Collaborating in publication of scientific research. Ensuring that study results and conclusions are scientifically sound, clearly presented, and consistent with statistical analyses provided. Clearly explaining statistical concepts, enabling non-statisticians and biomarker collaborators to use existing tools and interpret results better.

  • MS (with 6-10 years of experience) or PhD (with 2-6 years of experience) 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.
  • High degree of technical competence and effective communication skills, both oral and written.
  • Able to identify data or analytical issues, and assist with providing solutions by either applying own skills and knowledge or seeking help from others
  • Able to build strong relationship with peers and cross-functional partners to achieve higher performance. Highly motivated to drive innovation by raising the bar and challenging the status quo
  • 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.
  • Have strong leadership skills and experience in working with cross functional teams
  • Pharmaceutical or related industry knowledge desired, including experience and understanding of drug development and life-cycle management in the regulated environment.