Causality Biomodels
https://causalitybiomodels.comAbout
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Python
React.js
Git
Javascript
Amazon Web Services (AWS)Causality Biomodels is an Indo-German life science informatics company that focuses on the development of data-based solutions in the bioinformatics sector. Specifically, we work using semantic integration & information extraction methods, knowledge & data organization, and advanced statistical & machine learning techniques in the context of life sciences.
The team Causality Biomodels is searching for a full stack developer with a strong focus on Python, capable of taking on a lead developer role.
You will be mainly focusing on the following areas:
- Implementing new features by modifying our backend system and UI according to the product backlog and discussions with the team.
- Rapid prototyping to explore new directions based on current research developments.
- Design, development and maintenance of APIs, as well as product and add-on components.
- Maintenance of code integrity and organization.
The requirements are:
- Successfully completed bachelor’s or master’s degree in computer science or in related fields such as Bioinformatics.
- At least 2 years of professional software engineering experience.
- High proficiency in Python and ability to write clean and well-documented code (must).
- Experience with cloud-based development using AWS (preferred), GCP or Azure.
- Experience with Docker and container-based deployment.
- Proficiency in JavaScript.
- Experience with at least one database system (SQL or no-SQL).
- High familiarity with Git.
- Experience with agile development practices.
- Experience with CI/CD and automated testing.
- Very strong English skills (both verbal and written).
Bonus points for:
- Knowledge about machine learning or data science.
- Experience with Python packages SpaCy, scikit-learn, flask and fastapi.
- Experience with JavaScript libraries React and Redux/Context.
- Experience with Gitlab CI/CD pipelines.
- Experience working with knowledge graph data.
- Knowledge and experience in bioinformatics methods.
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