Data Scientist
- 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.
- 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.
- 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
- Strong scientific and quantitative acumen, with a demonstrated ability to integrate this acumen into platform & model building
- 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.
- Working knowledge of quantitative data pipelines, including workflow orchestration, ETL processes, quality control management, and reporting.
- Knowledge in at least one ML framework (PyTorch/TensorFlow/Jax).
- Effective working in fast-paced, highly collaborative, and matrixed teams.
- Exceptional communication skills, especially with requirements gathering and data presentation.
- Familiarity with immunology, vaccinology, or infectious disease concepts.
- Previous experience in a biotech startup.
- Experience building technology to process high-throughput sequencing (HTS) assays, preferably display-based HTS systems like phage, yeast, and/or mammalian cell surface display.
- Hands-on experience with Databricks, including cluster management, Unity Catalog–based data workflows, Spark/PySpark, and developing SQL queries/dashboards.
- Knowledge of anomaly detection methods, structural biology visualizations, and data modeling.
- Familiarity with cloud native tools such as AWS s3 and ec2.
- Understanding of DevOps, change management, and technical documentation concepts.
- Ability to communicate effectively across various platforms, such as Jupyter notebooks, PowerPoint, Teams, and verbal discussions.
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