Job Description

Description:

As an active team member of the Computational Biology and Digital Sciences (CBDS), the successful candidate will contribute to oncology drug discovery research through in silico data driven approaches. You will leverage multi-modal omics data analysis and collaborate with biologists to solve scientific challenges to advance drug discovery programs.

Duties and responsibilities:

• Strong scientific understanding and experience in bioinformatics analysis and their implications in disease biology
• Working knowledge of NGS based omics data analysis (bulk and single cell RNA-seq, spatial transcriptomics analysis is a plus)
• Identify and process publicly available and internal generated bulk, single-cell and spatial transcriptome datasets using statistical and bioinformatics techniques to create meaningful biological insights
• Apply and develop innovative analysis approaches when standard methods are not adequate
• Follow relevant scientific literature to ensure use of optimal methods and understand emerging practices across the field
• Interpret and present analysis results to coworkers, biologists and collaborators. Communicate work effectively orally and in writing
• Ensure FAIR data analysis with clear documentation and reproducibility
• Report and treat data with a high level of integrity and ethics
• Comply with applicable regulations; Maintain proper records in accordance with SOPs and policies

Skills:    

• Programming experience with two or more programming languages including: Python, R for bioinformatic data analysis, shell/bash programming in unix-like systems.
• Proficiency in working with bulk and single cell NGS data, spatial transcriptomic data analysis is a plus
• Proficiency in working with cloud computing and high performing clusters (HPC)
• Experience in biological pathway analysis
• Experience in applying machine learning and artificial intelligence methods to biological data, deep learning and graph learning is a plus
• Two years hand on experience of bioinformatic data analysis
• Solid background in basic biology and disease biology. Knowledge in oncology or immunology is a plus.
• Ability to develop and benchmark machine learning algorithms
• Experience in using common public oncology datasets is a plus (TCGA, GTEX, Human cell atlas, CZ CELLxGene Discover, Human tumor atlas network, etc)

Education:    

• PhD/Master’s degree from an accredited institution with experience in a related scientific discipline (Computational Biology, Genomics, Biostatistics, Bioinformatics and Biological Sciences preferred)

Pay Rate Range: 40-50/hr depending on experience 

Job Tags

Remote job, Contract work,

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