s person will be responsible for performing hands-on exploratory and regulatory related biomarker analysis, large-scale genetic, genomic, and other ‘omic analyses, with a primary goal of identifying and validating targets and biomarkers in preclinical studies and clinical trials for oncology. More specifically, this person will participate the design of genomic studies in preclinical and clinical research, implement cutting-edge informatics and statistical methods and tools to analyze the data including WGS/WES/targeted sequencing, RNAseq, epigenetic data, etc, and perform integrative analysis of genomic data and available clinical data in clinical trials. Furthermore, the individual will leverage publicly available cancer genomic and pharmacogenomics data to integrate with Eisai internal data for biomarker analysis. All analysis work will be documented and will follow best practices for enabling reproducible research.

Principal Duties and Responsibilities:
• Perform data analysis activities including cleaning, handling, integration, and analysis of small- and medium-scale experimental data generated from cell lines, model organisms, and human subjects, including genotype, mRNA, protein, and other data types.
• Analyze large-scale analysis-ready datasets to answer specific focused questions of relevance to project teams.
• Contextualize findings from internal experiments and generate hypotheses for new internal experiments by analyzing and interpreting externally available data.
• Provide appropriate visualization and interpretation of results in preclinical experimental data to support decision making on drug development projects.
• Contribute to preclinical experimental study design to generate statistically meaningful information.
• Prepare and track documentation including analysis plans, result reports, and progress reports.
Job Qualifications

• Ph.D. degree in Biomedical Sciences, Bioinformatics, Computational Biology, Biostatistics, Statistical Genetics, Genetic Epidemiology, or closely related field. At least 5 years of experience in hands-on cancer genomic data analysis.
• Must have analytic and statistical skills to conduct analysis of WGS/WES, transcriptomic, proteomic and epigenetic data. Must have experience in clinical outcome analysis.
• Must have a good understanding of human biology. Experience in oncology is highly desirable. Must demonstrate good understanding of drug discovery requirements and processes.
• Must have strong organizational skills and ability to prioritize work.
• Must have excellent communication skills and the ability to act on cross-disciplinary teams; must have demonstrated ability in scientific publications.
•Must have experience with R and Unix/Linux systems. Additional experience with languages/software including Spotfire, Bioconductor, Pipeline Pilot, and SQL is strongly preferred. Ability to use Excel and Python may also be useful.