As a Research Software Engineer / Data Scientist, you will provide the technical backbone for a global neuroscience collaboration. You will ensure the completion of the technical aspects of our scientific projects from experimental hardware to final scientific analysis, maintaining rigorous engineering standards while directly contributing to the field's body of research. You will leverage AI-driven methodologies to automate complex data workflows, accelerating the transition from raw neural signals to biological insights.
Key Responsibilities
Software Engineering. Develop, deploy, maintain and operate software and hardware to collect and analyze experimental neuroscience data at worldwide partner sites.
Scientific Contribution. Assist collaboration scientists to analyze data and co-author scientific publications.
Technical team player
Quality Assurance. Promote ML and software best practices including data versioning, reproducibility, version control, testing, packaging..
AI for Science Acceleration. Experiment and promote AI tools in the data analysis process. Ensure AI-driven results are accurate, reproducible and adhere to high ethical and integrity standards.
Communication. Communicate around the software infrastructure and produce materials such as documentation, tutorials and courses for both internal and external audiences.
Skills and Attributes for Success
Ability to design, run, and analyze experiments independently
A meticulous approach to data quality, ensuring datasets are clean, validated and ready for scientific usage
A commitment to engineer reliable and scalable solutions
Strong collaborative and problem-solving abilities
Minimum Qualifications
Master’s degree in Computer Science, Neuroscience or related area of study
Expertise: 2+ years of experience in systems neuroscience.
Professional proficiency in Python
Preferred Qualifications
Direct experience in systems neuroscience
Hands-on experience with ETL/ELT pipelines and data orchestration
A track record of contributing to or maintaining open-source projects.
Proficiency in a deep learning framework (PyTorch or JAX) and familiarity with foundation models or neural data transformers