Role purposeWe are looking for a highlymotivated computational scientist to help drive hypothesis-based trait lead discoveryfor important agronomic traits and breeding. In this role, you will help analyze and extract meaningful informationfrom complex biological data to identify target genomic elements and helpdesign a modification strategy for trait improvement. As a computationalscientist, you will work independently to manage, analyze, integrate, minecomplex biological data, and help interpret results for biological hypothesisgeneration. By partnering with crop experts, experimental and computationalbiologists in research teams, you will help deliver customized bioinformaticssolutions and contribute to the development of innovative seeds products. AccountabilitiesAnalyze large scale omics datasets, interpretand communicate analysis results to stakeholdersIntegrate diverse types of biological data toidentify leads for trait improvementWork closely with other computationalscientists and trait scientists to help interpret data and generatehypotheses on how candidate gene function impacts traits of interestContribute to the design of strategies onvalidating the function of candidate genesProvide consultations on experimental design, analyticalmethods, computational tools, and data interpretationReaching beyond standard functional genomicsapproaches, explore new machine learning based approaches for leadidentification Critical success factors & key challengesAbility to understand the science and biology underlyingtrait projectsAbility to translate and tackle biological challengeswith computational tools and methodsAbility to combine knowledge in diverse fieldsof biology, with skills in data analysis, data integration and data miningfor gene functional annotation and lead gene discoveryWork effectively in a cross-functional, cross-geographicalteam Keep abreast of the latest computationalmethods/techniques and how those can be integrated into novel researchdirectionsCritical knowledgeMaster’s degree with at least two yearsexperience or Ph.D. in Bioinformatics, Computational Biology, Statistics,Computer Science or a related field with experience on biologicalproblems; or Biology related fields with computational experienceExpertise in omics data analysis, data integration, and datamining Familiarity with Genome Editing CriticalexperienceAt least two years experience applyingcomputational analyses and tools to address biological questions,preferably in agricultural research Experience mining large scale omics and geneticdata for trait lead discovery is highly desiredExperience analyzing omics data (such as genome sequencing,transcript profiling, proteomics, or metabolomics data)Understanding of molecular biology and genetics, preferably withplant experience and knowledge of plant breedingExperience collaborating effectively acrossdisciplines Critical technical, professional and personalcapabilitiesKnowledge of genomics (such as genome assembly,annotation, pangenome, comparative genomics, functional genomics, etc)Experience in data integration, data mining,using and/or managing databases Experience working with UNIX/LINUX environmentExperience with machine learning, naturallanguage processing is a plusProficiency in applying statistical methods (suchas differential expression analysis, gene network analysis, gene setenrichment analysis, integrated omics analyses, etc.) for analyzing highdimensional biological data sets, such as RNAseq or metabolomics dataProficiency in one or more statisticalprogramming or scripting languages such as R, Python, or other relevantlanguagesDemonstrated ability to prioritize multipletasksAbility to learn and develop new technicalskills, and expand knowledge Excellent communication skills, fluency in bothverbal and written English communication Critical leadership capabilitiesAbility to drive the application ofcomputational analysis for trait lead discoveryAbility to work effectively in project teamswith members from diverse functions, disciplines, and/or geographicalregionsContribute ideas and thoughts, able toinfluence decision-making across a matrix organizationAbility to adapt to changes and help connectacross different R&D sites

上班地址：中关村生命科学园 先正达