Nonclinical Statistician Position at Pfizer
jobs
drug discovery
Pfizer
nonclinical statistics
The Research Statistics group collaborates across a wide variety of activities in the early phases of drug discovery. This position is located in Groton CT and has a focus on the optimization of chemical matter and the development of assays to characterize these molecules
The successful candidate will:
- Demonstrate leadership in influencing and improving drug discovery by identifying, developing, and applying new quantitative methods.
- Proactively seek collaborations with scientists and lab heads.
- Collaborate with scientists to plan meaningful studies, statistically analyze, and communicate / document the results.
Requirements:
- M.S. or Ph.D. in Statistics, Biostatistics, or related field and 2+ years statistical consulting experience in drug discovery and development, preferably in a laboratory science environment.
- The ability to proactively seek collaborations with scientists and lab heads.
- Strong initiative, excellent interpersonal and communication (written and verbal) skills
- Understanding of inference and probability; competence in contemporary linear modeling including mixed models, nonlinear regression; and predictive modeling/machine learning.
- Solid understanding of experimental design
- An understanding of tools for the analysis of high dimensional data
- Strong computational skills in R
Desired:
- Five or more years experience in the pharmaceutical industry.
- Sound understanding and experience of applying Bayesian methods
- Formal training in, or thorough understanding of: human physiology, cell biology, pharmacokinetics and/or pharmaceutical chemistry.
- Strong computing skills in scripting languages, such as perl, python, unix shell scripts or others. SQL and LaTeX skills are also advantageous.
Applying
The position is posted at http://pfizercareers.com/
- Go to “Search jobs” on the green tab
- Choose “SEARCH and APPLY for Jobs”
- In the “Advanced Search” bar, search on Job Id 1024297
(This article was originally posted at http://appliedpredictivemodeling.com)