Job:Abbvie -- Senior Manager/Associate Director, Exploratory Statistics
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Feng Hong • 0
Last seen 7.3 years ago
United States

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Senior Manager/Associate Director, Exploratory Statistics-14000001AH 

Location: Worcester, Massachusetts, or Lake County, Illinois, USA

 AbbVie (NYSE:ABBV) is a global, research-based biopharmaceutical company formed in 2013 following separation from Abbott Laboratories. The company's mission is to use its expertise, dedicated people and unique approach to innovation to develop and market advanced therapies that address some of the world's most complex and serious diseases. AbbVie employs approximately 26,000 people worldwide and markets medicines in more than 170 countries. 


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 (grade level and position title commensurate with experience) reporting to the Global Head, Exploratory Statistics, and embedded/co-located with the scientific collaborators. This position can be based in Worcester, MA or in North Chicago, IL.

Key Responsibilities:

  • Help build statistical capabilities in the Immunology Clinical and Discovery 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, 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.
 Equal Opportunity Employer Minorities/Women/Veterans/Disabled 



  • 5-20 years of 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 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, opens to suggestions and experimentation for improvement
  • Embraces the ideas of others, nurtures innovation and manages innovation to reality


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