工作职责：

1. Develop bioinformatics pipelines and workflows to extract value from clinical trials data and large multidimensional datasets originating from highthroughput experiments (e.g. omics data) to accelerate oncology drug discovery.
2.Develop and implement state-of-the-art computational methods and data mining strategies to address key challenges in oncology drug discovery including indication selection, patient stratification, biomarkers of response,drug resistance, tumor heterogeneity.
3.Analyze and integrate proprietary internal data with public resources such as TCGA to establish the local knowledge base as well as web server.
4.Understand key challenges and requirements in alignment with the work of multi-disciplinary teams, formulating testable hypotheses and translate them into data analysis challenges and solutions. Identify opportunities to support projects.
5. Ensure scientific excellence in analyses, software, pipelines and results. Be efficient, practical, collaborative and proactive at delivering well-documented reproducible work and proactive engage in knowledge sharing and peer support.

任职资格：

1.PhD or equivalent experience in bioinformatics, computational biology,biostatistics, or related field. A strong background in data analysis and statistics will be required.
2.A minimum of 2 years of relevant experience in a similar role and a good understanding of cell and molecular biology, human physiology and oncology
drug discovery.
3.Expertise statistical analysis and integration of omics datasets along with clinical and phenotypic data in an oncology environment.
4.Proven excellent data visualization and interpretation skills and strong communication skills, with technical and non-technical collaborators.
5.Proficiency in R, Python and Bash; competency working in a Linux HPC cluster environment (e.g. Slurm, SGE, PBS); experience with development
tools (e.g. Rshiny, Git, Conda, Docker), pipeline tools (e.g. Snakemake,Nextflow), databasing approaches (SQL and NoSQL), bioinformatics tools (e.g. Omicsoft, Bioconductor) and biological databases (e.g. IPA, KEGG,TCGA, cBioPortal).