Nr ref.: LP/SPD/ZD/10
Genomics Platform - Python SENIOR software engineer (Data Science and Statistical Computation group) with engineering background.
Collaboratively and pragmatically solve scientific software engineering challenges encountered at the forefront of single cell genomics data analysis infrastructure.
- Work with business analysts, computational scientists, and other software engineers to understand and conceptualize the complex, emerging needs of our scientists, whether they are working at the keyboard or the bench.
- Plan and prioritize complex scientific software projects in conjunction with collaborators, steering committees and other stakeholders.
- Collaborate with local and off-shore engineering teams to support your software development efforts.
- Effectively communicate strategies, ideas, goals and progress to departmental, cross-functional and senior management.
- Contribute to the broader scientific community through open-source software development.
- At least 5+ years of experience in Python (numpy, scipy, scikit-learn, tensorflow, matplotlib)
- At least 6+ years of experience in SQL
- At least 4-year experience in software development
- Advanced skills and experience with version control and JIRA-like task management
- Ability to communicate effectively
- English at least at B2 level
- BS or higher in bioinformatics, computer science or related fields technical university prefered.
- 3+ years experience developing tools for data analysis.
- Expertise in the Python data analysis ecosystem, including numpy, pandas, scipy.sparse and cython.
- Expertise with performant file formats for data analysis and scientific computation (e.g., hdf5, zarr, tiledb).
- Experience deploying performant software in cloud environments
- Familiarity with modern Machine Learning python frameworks (e.g, PyTorch)
- Familiarity with best practices for effective use of High Performance Computing environments (e.g., slurm).
- Experience supporting data science activities using FAIR data management and reproducible practices.
- Demonstrated adherence to best practices in software engineering, particularly usability, version control, testing, and appropriate use of abstraction.
- Demonstrated ability to effectively communicate about complex bioinformatics problems to peers, users and leadership.
- Biological domain knowledge is desirable but not required.
- Familiarity with formal build/release/deploy and continuous integration frameworks is a plus.
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