The Jackson Laboratory for Genomic Medicine is seeking a senior level computational scientist in Biostatistics & Statistical Genetics to join our Computational Sciences – Statistics & Analytics (CS-SA) group. In this role, you will have an excellent opportunity to make leading contributions to cutting edge research in disease biology and translational research in collaboration with the faculty and genetic resource scientists and clients of The Jackson Laboratory. Cross campus and industry collaborations are encouraged.
The Computational Scientist reports to the Director of Jackson’s Computational Sciences (CS) and have primary responsibility for providing statistical and analytical bioinformatics expertise and data interpretation to the scientific research programs at the Jackson Laboratory for Genomic Medicine in Farmington, CT and its collaborative programs. A Computational Scientist is expected to independently establish collaborations within The JAX, provide expert reasoning to the projects relevant to his disciplines, make leading contributions to the grant applications in collaboration with the researchers at The JAX.
The ideal candidate will:
• Have a Ph.D. in Biostatistics and Statistical Genetics
• Have a proven track record and enthusiasm for working in a dynamic high performance research team environment;
• Demonstrate the aptitude and capacity for leading statistical genetics and biostatistics effort at the Computational Sciences and its collaborators.
• Strong experience and publication record in Biostatistics and Statistical Genetics esp. in the field of clinical trials
• Be creative contributors eager to learn new technologies and science
Incumbents are required to live and work in Connecticut with periodic multi-day work visits to the Bar Harbor, Maine campus.
- Experience in Biostatistics and Statistical Genetics
- Experience in clinical trial design, analysis and report generation.
- Experience in High Throughput Sequence (HTS) data analysis, microarray data analysis, experimental design, data integration, algorithm development, development of sequence analysis tools (bioinformatics programming), evaluation of analytical tools and technology, and delivering training to the research community.
- Experience in statistical packages (e.g. R/SAS) and other commonly used software packages such as DAVID and IPA.
- Experience in developing computational algorithms and systems to support genetics and genomics research.
- Excellent communication skills including skills necessary to present at the conferences and workshops, write study designs and analytical methods.