- Role involves support for the integration, organization, and analysis of large and complex multi-omics datasets, but not limited to transcriptomics, genetics, and epigenetics.
- A vital part of this role includes ensuring the precision, quality, and comprehensiveness of our dataset repositories.
- The successful candidate will work in synergy with cross-functional and inter-organizational teams of research scientists. They will apply, refine, and expand established workflows for effective multi-omic data curation and analysis.
Key Responsibilities:
- Curate and standardize GWAS summary statistics datasets ensuring accuracy and consistency.
- Annotate genomic variants using established tools and databases.
- Map SNPs to genes and determine their functional relevance.
- Analyze transcriptomic datasets, including but not limited to bulk and single-cell RNA sequencing, to derive actionable insights.
- Collaborate intimately with research teams to comprehend specific data requirements and applications.
- Curate, maintain, and contribute to the project codebase.
Qualification:
- Masters with 1+ year of experience or PhD degree in the quantitative sciences (Bioinformatics, Computer Science, Computational Genetics, Mathematics, Statistics, or a related field).
- Proven capability in designing and implementing bioinformatics workflows and corresponding github codebase repositories.
- in R programming and comfortable operating in a Unix/Linux and High-Performance Computing (HPC) environment.
- Familiarity with genome-wide association studies, particularly in fine mapping and colocalization from GWAS summary data is expected.
- Hands-on experience in curating and processing transcriptomic datasets, especially bulk and single-cell RNA-Seq, with the ability to conduct corresponding downstream analyses would be advantageous.
- Knowledge and proficiency in basic statistical concepts, e.g. p-value, odds ratio, effect size, multiple testing correction, familiarity with Bayesian approaches employed for colocalization analysis would be advantageous.
- Previous experience in immunological diseases or rare disease genetics is a plus.
Job Type: Contract
Pay: $40.00 - $42.00 per hour
Expected hours: 40 per week
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Paid time off
- Vision insurance
Schedule:
Experience:
- GWAS / Genome-wide association study: 2 years (Preferred)
- HPC / High cloud-computing: 2 years (Preferred)
- Bioinformatics: 2 years (Preferred)
- computational biology: 2 years (Preferred)
Work Location: Remote