Position Location: South San Francisco, CA
As a bioinformatics scientist / engineer developing applications in oncology, personalized medicine, inherited disease, and viral/microbial genetics, you will play a key role in developing, improving, and expanding analysis solutions for next-generation DNA sequencing products. Primary responsibilities will include programming (Java, Python), developing algorithms and software, building models, and analyzing data for analysis of next-generation DNA sequencing. The successful candidate will be part of a multi-disciplinary team responsible for developing and launching cutting edge applications for DNA sequencing instruments to advance genomic research, especially in oncology and personalized medicine. The candidate will prototype, adopt, improve, develop, optimize, perform training and documentation, and validate algorithms and analysis software to analyze DNA/RNA/cfDNA sequencing data for oncology, pharmacogenomics, metagenomics, Immuno-Oncology and other applications. The candidate will intensively analyze data, troubleshoot, draw conclusions, and visualize, document, and present their conclusions. The candidate will work with project teams and software teams. The candidate will be expected to work in a fast paced, complex setting to hard deliverables in a collaborative environment. Critical thinking, determination to succeed, focus on quality and innovation, the willingness and courage to push the boundaries on the possible and the drive to deliver are key attributes.
Responsibilities
Maintain, develop, optimize, troubleshoot, extend, validate, test, provide training, and document production-level software
Support applications such as gene fusion detection, RNA analysis, microbial and viral genetics, and Pharmacogenomics.
Analyze data, perform statistical analyses, archive data, do troubleshooting and root cause analysis, build research tools, in multi-disciplinary teams, as part of development and validation of targeted sequencing assays for multiple applications, especially Oncology, used in research and clinical research settings worldwide.
Work closely with molecular biologists, product managers, program managers, other bioinformatics colleagues, and software engineers to build high quality products.
Develop prototypes quickly, integrate these methods into production software, and collaborate with key internal and external groups.
Keep up to date with the latest developments in algorithms for analysis of next-generation sequencing and applications of sequencing and other developments in genomics, especially for personalized medicine and human health. Give internal and external presentations.
Requirements
- PhD in computer science, computational biology, biostatistics, bioinformatics, or similar (MS considered for the candidate with the right experience).
- A background in DNA sequencing technologies, bioinformatics, genetics, biology.
- Experience with Algorithm development / Scientific computing. Hands-on experience in developing bioinformatics analysis methods/algorithms for sequencing data is strongly preferred
- Demonstration of rapid prototyping experience, leading to development of production code that is robust, fast, and highly accurate is strongly preferred.
- 1-5 years of work experience or equivalent PhD or post-doc experience
- Strong programming experience on a UNIX platform in relevant languages, for example Python, Java, or similar
- Experience with Matlab or R is desired
- Experience building models in complex arenas, including recent relevant experience
- Ability to engage in clear written and oral communication
- Excellent problem solver, analytic thinker, team player, and quick learner
Preferred Qualifications
- Previous industry experience, familiarity with common bioinformatics tools, experience with high performance computing, understanding of primer/assay design are all desirable.
- Experience in any of the following are highly desirable: cancer biology, immunology, immuno-oncology, heme-onc, human genetics virology, metagenomics.
- Experience with clinical testing, are all highly advantageous. Strong background in statistics, machine learning, data science are desirable.
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