Scientific Data Scientist

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Arctoris is seeking a Senior Scientific Software Engineer / Data Scientist to design and implement analytical pipelines and peripheral software architectures and tools that power data-driven drug discovery. This role is ideal for a data scientist and/or developer with strong foundations in scientific data engineering, bioassay data analysis, and full-stack software design, who thrives at the intersection of computational biology and software engineering.

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• Architect, develop, and maintain analytical and visualization pipelines for diverse bioassay data and project requirements.
  
• Design and build pragmatic scalable scientific tools and data processing frameworks to support Arctoris’ experimental platforms.
  
• Implement interactive GUIs and web applications for experimental data management, decision support, and visualisation.
  
• Develop robust, maintainable codebases and contribute to best practices in software design, CI/CD, testing, and documentation.
  
• Collaborate with data scientists, biologists and drug discovery experts to provide meaningful data products and insights.
  
• Optimise integration between lab automation systems, databases, and analytical pipelines.
  
• Contribute to data infrastructure evolution, including APIs, orchestration layers, and cloud architectures.
  

You have a track record of building performant software for data-rich scientific environments. This role bridges analytical insight with engineering rigor, designing data systems that accelerate hypothesis generation and validation in modern drug discovery. With this in mind, we are seeking someone who is motivated by impact, precision, and the application of software engineering to real-world biology.

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• Advanced proficiency in Python for scientific and analytical computing, including: Pandas, NumPy, Scikit-learn, Jupyter, RDKit, Biopython
  
• Experience building data ingestion, curation, and transformation pipelines.
  
• Expertise in software engineering principles: modular design, testing (PyTest/Unittest), and version control (Git).
  
• Strong command of data modeling and database design (PostgreSQL, MySQL, MongoDB, or equivalent).
  
• Proven experience in web application frameworks (FastAPI, Django, or Flask) and modern front-end technologies (React or Vue).
  
• Familiarity with scientific workflows and bioassay data structures common in drug discovery.
  

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• Knowledge of data orchestration frameworks (Prefect, Airflow, Jenkins).
  
• Experience deploying systems on AWS, Azure, or GCP.
  
• Proficiency in containerization and CI/CD (Docker, GitHub Actions, Bitbucket Pipelines).
  
• Understanding of DevOps practices, cloud infrastructure automation (Terraform, Ansible), and scalable API architectures.
  

Exposure to lab informatics, LIMS, or automation system integration.