锦鲤求职岗位描述
About Us / 关于我们
Join our interdisciplinary team of top-tier bioinformaticians, AI scientists, and drug development experts dedicated to accelerating drug discovery, preclinical research, and precision medicine through innovative algorithms and digital tools. We combine cutting-edge computational methods with deep biological insight to drive breakthroughs in life sciences.
加入我们由顶尖生物信息学家、AI科学家与药物开发专家组成的跨学科团队，致力于通过创新算法与数字化工具加速药物发现、临床前研究与精准医疗。我们将前沿计算方法与深厚的生物学洞见相结合，共同推动生命科学领域的突破。
Role Description / 职位描述
As a Bioinformatics Scientist, you will design, develop, and deploy advanced bioinformatics and AI/ML solutions to analyze and interpret complex multi-omics data. You will translate biological and medical questions into computational approaches—from pipeline development and algorithm design to actionable insights—enabling data-driven discovery in life sciences.
作为生物信息学科学家，您将设计、开发并部署先进的生物信息学与AI/ML解决方案，用于分析和解读复杂的多组学数据。您将把生物学与医学问题转化为计算方法——从流程开发、算法设计到可落地的科学洞察，推动生命科学领域的数据驱动发现。
Key Responsibilities / 核心职责
Analyze complex biological data (genomics, transcriptomics, proteomics, metabolomics) and develop customized bioinformatics pipelines for multi-omics integration.
分析复杂的生物学数据（基因组、转录组、蛋白组、代谢组等），开发定制化的生物信息学流程，实现多组学数据整合。
Design, develop, and deploy scalable machine learning and statistical models tailored to biological questions, such as differential expression, variant detection, pathway enrichment, and biomarker identification.
设计、开发并部署面向生物学问题的可扩展机器学习与统计模型，例如差异表达分析、变异检测、通路富集与生物标志物识别。
Collaborate closely with experimental biologists, AI scientists, data engineers, and product managers to ensure data quality, interpret findings, and translate computational outputs into biological and medical insights.
与实验生物学家、AI科学家、数据工程师及产品经理紧密协作，确保数据质量、解读分析结果，并将计算输出转化为生物学与医学洞见。
Maintain high standards of code quality, reproducibility, documentation, and data governance in compliance with research and industry best practices.
保持高标准的代码质量、可复现性、文档规范与数据治理，遵循研究及行业最佳实践。
Stay current with emerging technologies in bioinformatics, AI/ML, and cloud computing, and evaluate their application to ongoing projects.
持续关注生物信息学、AI/ML与云计算领域的新兴技术，并评估其在现有项目中的应用潜力。
Daily Work / 日常工作
End-to-End Bioinformatics Solution Development: Build and maintain robust, production-grade pipelines for NGS and multi-omics data processing, visualization, and interpretation.
端到端生物信息学方案开发：构建并维护稳健、生产级的NGS及多组学数据处理、可视化与解读流程。
Advanced Algorithm Design: Research, develop, and optimize algorithms and models for biological sequence analysis, structural prediction, single-cell analytics, and knowledge graph-based data integration.
高级算法设计：研究、开发并优化用于生物序列分析、结构预测、单细胞分析及基于知识图谱的数据整合算法与模型。
Data Pipeline Collaboration: Work with data engineers to design and implement scalable data architectures and ETL processes that support model training and real-time bioinformatics applications.
数据管道协作：与数据工程师协作，设计并实施可扩展的数据架构与ETL流程，以支持模型训练与实时生物信息学应用。
Insight Communication: Create clear, compelling visualizations and reports to communicate complex analytical results to both technical and non-technical stakeholders.
洞察传达：创建清晰、有力的可视化图表与分析报告，向技术及非技术相关方有效传达复杂的分析结果。
Key Qualifications / 任职要求
PhD or Master’s degree in Bioinformatics, Computational Biology, Genomics, Statistics, or a related field (exceptional MSc candidates with substantial experience will be considered).
生物信息学、计算生物学、基因组学、统计学或相关专业博士或硕士学位（经验丰富的优秀硕士也将被考虑）。
3+ years of hands-on experience in bioinformatics, NGS data analysis, and multi-omics data integration, preferably within biotech, pharma, or a CRO environment.
3年以上生物信息学、NGS数据分析与多组学数据整合的实践经验，具备生物技术、制药或CRO行业经验者优先。
Strong foundation in molecular biology, genomics, and statistics; familiarity with drug discovery or preclinical research workflows is a plus.
扎实的分子生物学、基因组学与统计学基础；熟悉药物发现或临床前研究流程者优先。
Excellent communication and teamwork skills, with the ability to collaborate across biology, AI, and engineering teams.
出色的沟通与团队协作能力，能够跨生物学、AI及工程团队有效合作。
Proficiency in Mandarin and English is preferred.
优秀的中英文能力者优先。
Technical Expertise / 技术专长
Proficiency in programming languages such as Python, R, or Perl, with advanced Linux/Unix environment skills.
精通Python、R或Perl等编程语言，具备熟练的Linux/Unix环境操作能力。
Expertise in bioinformatics tools and databases (e.g., GATK, STAR, Bowtie, DESeq2, KEGG, GO, UCSC, Ensembl).
熟练掌握生物信息学常用工具与数据库（如GATK、STAR、Bowtie、DESeq2、KEGG、GO、UCSC、Ensembl等）。
Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and their application to biological data (sequence modeling, structural prediction, etc.).
具备机器学习框架（如TensorFlow、PyTorch、Scikit-learn）应用经验，并有将其用于生物数据（序列建模、结构预测等）的实际项目经历。
Hands-on experience with big data technologies (Spark, Hive), workflow managers (Nextflow, Snakemake), containerization (Docker/Singularity), and cloud platforms (AWS, Azure, GCP).
具备大数据技术（Spark、Hive）、流程管理工具（Nextflow、Snakemake）、容器化（Docker/Singularity）及云平台（AWS、Azure、GCP）的实战经验。
Understanding of data governance, including data quality, security, metadata management, and reproducible research practices.
理解数据治理，包括数据质量、安全、元数据管理与可复现研究实践。
Preferred Qualifications / 优先考虑
First-author publications in top bioinformatics or life science journals, or leadership experience in drug target discovery/clinical biomarker projects.
在顶级生物信息学或生命科学期刊以第一作者发表过论文，或具有药物靶点发现/临床生物标志物项目领导经验。
Familiarity with antibody/protein engineering, humanization, affinity maturation, or structural biology analysis.
熟悉抗体/蛋白质工程、人源化、亲和力成熟或结构生物学分析。
Experience with single-cell multi-omics data integration, knowledge graphs, or AI-driven drug screening models.
具备单细胞多组学数据整合、知识图谱或AI驱动的药物筛选模型相关经验。