Join a pioneering biotech company as a Scientist, developing treatments for unmet medical needs. This fully remote role offers ultimate flexibility and the chance to make a meaningful impact on healthcare advancements.
Job Detail
Job Description
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 understanding 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)
- ShareAustin: