Job Title: Bioinformatics Analyst
Location: South San Francisco, CA
Experience: 4-8 Years
Duration: 12 Months (possible extension)
We are seeking a Bioinformatics Analyst to support drug discovery programs with a focus on Cell Therapy therapeutics . The role involves analyzing large-scale genomic and NGS assay datasets, developing computational pipelines, and providing statistical insights that drive therapeutic candidate identification. You will collaborate closely with scientists and engineers to design studies, interpret results, and communicate findings across multidisciplinary teams.
Key ResponsibilitiesPerform quality control on sequencing and alignment data to ensure data accuracy and reliability.
Analyze large genomic and NGS datasets to extract meaningful insights for drug discovery.
Build, maintain, and optimize bioinformatics pipelines for scalability, reproducibility, and efficiency.
Develop and refine statistical models and hypothesis tests to interpret complex datasets.
Provide computational and statistical support for diverse drug discovery programs.
Collaborate with wet-lab scientists, engineers, and protein designers on study design and data interpretation.
Prepare and present data summaries, reports, and presentations to cross-functional teams and stakeholders.
Apply software development best practices in coding, version control, and documentation.
Strong background in statistics and software development .
Proficiency in Python , including use of open-source and custom packages.
Experience with NGS technologies and biological assays.
Familiarity with cloud computing platforms (AWS preferred).
Proficiency with Git for version control and reproducibility.
Ability to translate biological questions into rigorous computational analyses.
Prior experience supporting drug discovery in biotech or pharmaceutical environments.
Excellent communication skills, with the ability to present complex results to technical and non-technical audiences.
Strong collaboration skills, especially in guiding study design and avoiding experimental pitfalls.
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