Data Scientist, Data Analyst, Machine Learning Engineer
Experience Postdoctoral Researcher • University of Edinburgh ML Development : Designed Python (scikit-learn, PyTorch) models for bacteria-phage interaction prediction and E. coli host assignment which achieved accuracies of over 90% and revealing insights into AMR and phylogeny. Pipeline Engineering : Automated genomic data processing via containerized pipelines, streamlining model training/testing and outputting a comprehensive isolate report from raw data in 2 hours. Tool Deployment : Built an internal Flask web app for sequence analysis and ML predictions. Scalability : Leveraged HPC/cloud platforms (Google Cloud, CLIMB) to process over 8TB of genomic data, enabling large-scale
Temps plein / Uniquement Travail à distance
The University of Edinburgh・
Using machine learning to examine Salmonella enterica host specificity.