The Quantitative Sciences Unit (QSU) is an interdisciplinary collaborative statistics unit in the Biomedical Informatics Research Division within the Department of Medicine at Stanford University. The mission of the QSU is to facilitate cutting-edge scientific studies initiated by Stanford investigators by providing expertise in biostatics and informatics, to mentor and educate clinical investigators in research methods and to mentor data scientists. To optimally achieve this mission, the QSU members become fully integrated into individual research teams.
We are seeking a highly motivated bioinformatician or computational biologist who can collaborate with clinical and wet lab investigators with a high degree of independence.
· Identify and apply appropriate computational methods for biomarker discovery in the context of molecular data, for prediction of therapy response in cancer immunotherapy trials
· Create pipelines for the processing and analysis of high-throughput data
· Contribute to development of novel methods and resources in computational cancer biology
· Develop oral and written dissemination of findings for meetings with collaborators or for medical and/or statistical journal articles
· The incumbent may also be expected to develop lectures on computational methods for the training of other investigators including junior scientists
· In addition, the incumbent will participate in developing and writing grant proposals
· Ph.D. in Bioinformatics, Computational Biology, Systems Biology, Biostatistics, or related field
· Capable of functioning independently and collaboratively at an advanced level under the overall direction of the faculty lead
· Experience with programming languages in computational biology and biostatistics such as R (preferred), Python, Perl, Matlab, etc., in Unix environments
· Experience with low level data processing and cleaning for high-throughput data (e.g. QC, alignment, deduplication of NGS data)
· Knowledge of experimental design for generating high-throughput data such as next generation sequencing data (RNA-seq, DNA-seq, exome-seq, ATAC-seq), and proteomic data (especially luminex assays and CyTOF)
· Knowledge and experience of analysis of high-dimensional genomic/proteomic data such as next generation sequencing data (RNA-seq, DNA-seq, exome-seq, ATAC-seq), and proteomic data (e.g. luminex assays and CyTOF)
· Background in machine learning, data mining, statistical learning methods applied to molecular data (such as random forests, sparse regression); particularly in the context of biomarker discovery
· Outstanding oral and written communication skills with the ability to communicate technical information to all audiences
· At least two years of experience collaborating with clinical and experimental scientists
· Understanding of, and experience with, statistical analysis of clinical trial outcomes (in particular right-censored outcomes in the context of molecular predictors)
· Experience supervising technical staff including training, mentoring and coaching
· Experience developing grant proposals.