职位描述
职位描述:
Pre-clinical statisticians are responsible for supporting a wide variety of projects in the discovery portfolio in collaboration with biologists, chemists, and toxicologists. The expectation for this position is for the individual to understand the science & business, and to use her/his expertise in statistics to collaborate with scientists to develop novel targets, identify and optimize safe, efficacious compounds, and advance them through the drug discovery process.
The qualified candidate will be expected to do most of the following:
1. Become well-versed with the drug research and development process.
2. Closely collaborate with biologists, chemists and other scientists and support a variety of projects in the drug discovery portfolio, including in vitro and in vivo assay development, molecule development & optimization, experimental design and data analysis.
3. Leverage scientific & statistical expertise in collaboration with clinical statisticians, physicians and translational scientists to identify & explore tailoring therapeutic opportunities for Lilly compounds.
4. Educate the scientists on various statistical concepts and the use of software (JMP, R, etc.) to promote statistical application and ensure reproducibility of experimental results. Take the initiative, be proactive and communicate the value of good statistical practices to discovery scientists.
5. Develop/adapt/implement novel statistical methodology. Develop automated analysis approaches and tools.
6. Stay current with literature on statistical methodology, maintain proficiency in applying new and varied methods, and be competent in justifying methods selected.
Minimum Requirements:
? Ph.D. in statistics or related fields or M.S. in statistics with at least five years of industry experience (preferably pharmaceutical experience).
? Good theoretical and applied statistics background in statistical experimental design, ANOVA and regression methods such as linear and nonlinear models, mixed-effect models and expertise in modern analysis and visualization methods.
? Good working knowledge of either SAS/JMP or R/Splus.
? Strong background in Genetics/Genomics and statistical analysis of genomics data.
? Excellent communication skills and strong teamwork & leadership skills.
Other Requirements:
? Good theoretical and applied statistics background in predictive modeling and machine learning techniques, multivariate analysis and optimization methods, multiple testing theory and application.
? Background or formal education in topics such as Biology, Physiology, Chemistry, or relevant sciences is a strong plus.
? Experience with operating systems such as UNIX and Windows; Familiarity with other software/languages such as MS Office, GraphPad Prism, Perl, C++, Java, etc.
