Apriori Bio is seeking to improve vaccine design and increase efficacy and breadth by surveying viral landscapes using their technology platform Octavia™. The goal is to develop variant-resilient vaccines by predicting antigen evasion of existing immunity and leveraging high-throughput immune landscaping data.
Requirements
- Ph.D. in life sciences or a quantitative field.
- Proficiency in writing production-standard Python code, especially with packages in the scientific computing ecosystem (NumPy, SciPy, Matplotlib, pandas, etc.).
- Experience with analyzing and interpreting high-throughput experimental data
- Solid bioinformatics background with proficiency handling and analyzing sequence data
- Familiarity with collaborative coding using GitLab best practices and commands.
- General proficiency with cloud platforms, specifically AWS.
- Demonstrable extensive experience or study in statistical/quantitative modeling & machine learning.
Responsibilities
- Execute and manage high-throughput sequencing analysis pipelines
- Analyze and interpret results of high-throughput experiments, assessing relevance for associated programs and communicating evaluated results to program teams.
- Collaborate with wet-lab scientists to design experiments for assay development, program advancement, & expansion of the Octavia platform.
- Collaborate with broader Apriori team to develop and optimize new high-throughput assays to expand the capabilities & impact of Octavia
- Perform longitudinal data analyses to support experimental decision-making & platform development.
- Integrate various data sources to engineer useful features for predictive modeling & antigen design.
- Assist with organizing, extracting, and uploading proprietary experimental information programmatically with Benchling ELN’s SDK.
Other
- Create clear, user-friendly visualizations to be developed into dashboards and automated workflows.
- Regularly present platform progress in research and executive team meetings, creating comprehensive reports and slides for widespread communication.
- Uphold data management integrity and reproducibility through established software engineering best practices.
- Monitor cutting-edge technologies and models and identify potential collaboration opportunities across the Flagship Ecosystem.
- Effective working in fast-paced, highly collaborative, and matrixed teams.
