Summary/Contribution of Position:
The Computational Biologist will work as a core member of the R&D team leading the analytical aspects of product development with in-depth knowledge of a broad range of computational analytical tools and strategies to analyze multi-omic datasets. You will perform and/or supervise the performance of in silico experiments to support RNA research, Epigenetic research, Clinical applications, and other related service as required.
Essential Duties and Responsibilities
These responsibilities include but are not limited to the following:
- Develop advanced computational methods to enhance research and discovery on epigenomic, transcriptomic and genomic platforms.
- Enhance bioinformatic analysis of diverse NGS and non-NGS datasets, including RNA-seq, WGBS, Targeted sequencing, MicroArray etc.
- Develop innovative, robust, computational and statistics methods that can be applied in research and clinical settings.
- Provide intellectual and technical excellence to guide new discovery strategies. Design, perform, troubleshoot, and interpret experiments
- Present project updates, data analysis at technical and commercial meetings.
- Identify, develop, and manage collaborations and partnerships.
Education and Experience:
- PhD or Master in Bioinformatics, Computational Biology, Applied Mathematics, Statistics, Epidemiology, Biological sciences, Cancer biology, Genetics, Genomics, Computer science, or a related discipline with a strong record of publications/patents is required.
- Experience in the analysis of large next-generation sequencing date sets and microarray data.
- Hands-on experience on working with various public and proprietary data repositories, such as TCGA, GTEx, dbSNP, Ensembl, UCSC genome browser.
- Experience with manipulation and innovative analysis (e.g. novel algorithm development) of large biological and/or epidemiology data sets.
- Experience with statistics and/or machine learning.
- Extensive experience in bio-computational programming, scripting, querying or statistical analysis languages such as python or R, as well as Linux OS for high performance computing.
- Familiarity with Amazon Web Services EC2 and S3.
- Biological research experience.
- Familiarity with popular bioinformatics command-line tools.
- Experience with building and execution of bioinformatics pipelines.
- Experience with data science tools such as scipy, pandas, scikit-learn, or their equivalencies in R.
- Experience with data visualization tools such as matplotlib, seaborn, plotly, or their equivalencies in R.
- Scientific knowledge
- Interpersonal skills
- Team Player
- Communication skills - verbal and written
- Listening skills
- Problem analysis and problem-solving
- Attention to detail and accuracy
- Data collection and ordering
- Customer service orientation
- Stress tolerance
- Ongoing learning and Scientific Reading habits