Software Engineer (Bioinformatics)
CurrentSoftware Engineering: Experienced full-stack developer in independent and collaborative environments, using Agile methodologies with Kanban boards in Jira. Skilled in designing maintainable & robust software architectures balancing performance and user experience, integrating technologies like Okta for SSO authentication. Adherent to best practices including version control, code reviews, and CI/CD pipelines.Developed two internal Laboratory Information Management Systems (LIMS) essential to the daily operation of multiple laboratories with ~10-100 users. These are Django web applications which integrate with central institute systems (E.g. HR & Finance) and were built with the following tech stack: Django with MySQL, HTML, CSS/LESS, JavaScript, jQuery, HTMX, Git/GitHub, Docker, Kubernetes, and Jenkins.Led development of a GC-MS data analysis desktop application, evolving it from MATLAB to Python. Features complex GUI with ~100 concurrent graphs. Used MATLAB App Designer and Python PySide6, managing via GitHub. Application used across institutions, with global open-source release planned for later this year.Completed various smaller scripts requested by scientists to fit their specific needs in Python and Rust.Bioinformatics: Applied machine learning in metabolomics, assessing algorithms for batch effects, visualisation, and biomarker identification. Educated non-specialists on statistical techniques through presentations on t-SNE, PCA, PLS-DA, basic machine learning, and more.Implementing statistical analysis pipeline for proteomics STP. Experienced in Python and R for data analysis, using packages like Numpy, Pandas, Sklearn, TensorFlow, and PyTorch.Gained extensive knowledge in metabolomics and proteomics through lab embedding, training programs, and regular lab meetings and journal clubs.Attended ISMB/ECCB 2023, University of Cambridge statistical training, and upcoming ISCB conference. Completed NVIDIA deep learning course.