This Jobot Job is hosted by: Emily Olinger
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Salary: $130,000 - $180,000 per yearA bit about us:
We are a leading cloud based SaaS startup in the biotech industry. Our platforms leads to insights for improved diagnostics, targeted therapies, and better patient care. We recently completed a $100 million financing round to advance our growth globally to further serve leading healthcare and life science organizations! If you are interested in becoming a leader in the Biotech space, apply today! Location:
Mountain View, CA or FULLY REMOTEWhy join us?
- 100% of health premiums paid by employer for YOU, 75% paid for dependents
- 401K and Stock options
- Great company culture
- Remote options
- Rapidly growing company
- Develop and apply analytical approaches for large, complex genomic data sets in conjunction with clinical, phenotypic, and multi-omics data.
- Define solutions that meet customer requirements and research goals, working closely with program management and engineering team to drive those solutions through development, testing, and customer validation in an agile environment.
- Conceptualize and develop optimal methods/pipelines and Jupyter Notebooks for a diverse set of genomic data analysis workflows that allow domain scientists and savvy users alike to gain insights from large scale data.
- Analyze real-world datasets such as UK Biobank to understand the underlying data models and research goals.
- Research, integrate, test and validate new bioinformatics methods on our platform.
- Develop reusable, well-tested software in WDL, Python, R or shell scripting.
- Design data standards and integrate normalized genomics datasets on our platform.
- Ph.D. in computer science, bioinformatics, computational biology, genetics, or related discipline with a computational emphasis.
- Experience in bioinformatics, biostatistics, genomics, statistical genetics, population genetics, systems biology, and/or translational research in either academic or industry settings.
- Strong programming skills with the ability to develop reusable, well-tested software with advanced level knowledge in Python, R, and bash.
- Experience with cancer genomics such as working with somatic data and TCGA dataset.
- Experience with big data analytics technologies including Spark, Hive, and Hadoop, and an understanding of relational database concepts.
- Experience working with large-scale omics datasets, e.g. ENCODE, 1000 Genomes, ExAC/gnomAD, TCGA.
- Familiarity with statistical genetics methods and tools including GWAS (PLINK, HAIL, BOLT-LMM, SAIGE, RVtests, SKAT, METAL), PheWAS (PLATO, PHESANT), Polygenic Risk Score analysis (PRS), Mendelian randomization, fine mapping, pathway analysis.
- Understanding of cloud computing and high-performance computing.
- Data wrangling and understanding of big data ETL processes
- Large scale multi-omics data management
- Understanding of existing techniques for managing and analyzing genomic, clinical/phenotypic, pharmacokinetic, and other molecular data (transcriptomic, metabolomic, proteomic, microbiome), and the challenges in aggregating datasets for reuse in follow on studies.
- Familiarity with commonly used reference and annotation databases such as OMIM, ClinVar, gnomAD, and multi-omic QTL databases such as GTEx, eQTLgen, SPANR, and others.
- Familiarity with integrated tools such as GDC DAVE, cBioPortal, i2b2 tranSMART, Spotfire, UCSC Genome Browser, and Ingenuity Pathway Analysis.
- Knowledge of data file structures (data dictionaries, data files, codings CSV, and others) and their usage.
Interested in hearing more? Easy Apply now by clicking the "Apply" button.