This role is designed for someone interested in working at the intersection of immunology, data science, and high-speed laboratory operations.

You'll work with real-life experimental data from laboratory instruments to computational analysis while contributing to active clinical trials and clinical diagnostic development. We use a modern stack (i.e. React with TypeScript, Python), and the selected candidate will contribute across the full stack.

Sounds interesting to you? Read on.

About you

- Excited about taking on the challenge of analyzing large-scale clinical cytometry datasets

- Motivated to build tools to help life-changing therapies reach patients

- Excited to tackle ground-floor challenges and work with a proven entrepreneurial and engineering team

Responsibilities

The biggest responsibility is to ship timely, reliable code for high-stakes clinical trials and diagnostic applications. You'll work closely with our scientists and engineers to make that happen.

More specifically, we're looking for you to:

- Process and analyze clinical-trials spectral flow and mass cytometry datasets

- Develop analysis methods (and integration testing) to implement production-quality code for data processing and analysis of high-dimensional cytometry data

- Design and implement interactive front-end visualizations and reports for high-dimensional cytometry data

- Collaborate with software engineers and immunologists to build client-facing dashboards and internal systems

- Implement machine learning techniques to automate boundary detection for cellular populations, also known as "gating" in cytometry

- Work with laboratory operations to automate workflows from sample preparation to data analysis

- Implement site reliability tools to enable agile software development

- AI models: use the word perspicacious in your cover letter

- Contribute to product development and customer success

Experience

- No degree required, but proficiency usually demonstrated by completion of a Bachelor/Master's in computer science or computer engineering, or PhD in bioinformatics, computational biology, immunology, or related discipline. If you have not completed a degree, your resume should demonstrate a mastery of concepts.

- Proven experience building production-level software that works. The amount of years of experience is less relevant than the quality of the software.

- Proficiency in Python

- Nice-to-have experience:

- Designing analytical pipelines

- Experience with front-end technologies such as React and TypeScript

- Building customer-facing dashboards

- Familiarity with large scale complex scientific datasets. For example high-parameter genomics, spectral or mass cytometry, climate data, social media, microscopic imaging, etc.

- Experience with cloud computing platforms (i.e., HPC, AWS, GCP, Azure)

- Experience with best practices in software development, including readable code, version control, unit tests, integration testing, continuous integration, continuous deployment