Job Title: Bioinformatics Associate I (Remote)
Location: Remote
Duration: 3 - 4 months, (but not exceeding December 2024)
Start Date: ASAP
Description
- The Department of Biology Research & AI Development seeks a bioinformatics associate to serve a critical role on an internal project.
- This work will enhance our understanding of our vast collection of molecular data and will facilitate new downstream capabilities not possible without its implementation.
- You will be responsible for organizing internal data, collecting external data, implementing (and potentially enhancing) bioinformatics pipelines, and leading specific downstream analyses after the data is fully processed.
- This position is expected to be 100% (40 hours/week) time, and available for meetings during Pacific timezone if needed.
Responsibilities
- Develop systems for processing large amounts of internal data with established pipelines
- Locate useful external datasets and work with data ingest team to efficiently bring the public datasets in-house
- Communicate with BRAID and Data Management team members to ensure all data is being handled properly and efficiently
- Organize the storage and management of the raw and processed data
- Lead specific downstream analysis tasks and produce figures that can be used in final presentation of project
- Document all processes and maintain clear and detailed records of the data handling and analysis steps
Requirements
- Hands on experience working with NGS bioinformatics pipelines
- Familiarity with Python and data organization packages (e.g. pandas)
- Proficiency with UNIX/Linux operating system
- Strong organizational skills and attention to detail
- Enthusiastic about working in a scientific environment, especially one focused on drug discovery and development
- Have a flexible learning mindset and be able to work in a fluid and dynamic environment
- Demonstrated ability to effectively communicate about complex bioinformatics problems to peers, users and leadership
Additional Valuable Experience
- Experience with CellRanger and 10X scRNA-seq data processing
- Familiarity with workflow languages such as WDL or NextFlow
- Knowledge of basic machine learning concepts and experience with scikit-learn and PyTorch
- Experience with cloud computing platforms (e.g., AWS, Google Cloud) for large-scale data processing
- Understanding of single-cell RNA-seq data analysis techniques and tools
- Proficiency with version control systems such as Git