Developed and deployed a recommendation system that boosted online course enrollments

Architected scalable AWS environments and containerized machine learning models with Docker

Implemented MLflow and CI/CD pipelines to automate model tracking, versioning, and delivery

Built predictive models that enabled early identification of high-risk clients

Automated data pipelines and validation frameworks in Python to improve dataset quality

Developed monitoring scripts to track model accuracy, detect data drift, and trigger retraining

Designed CNN models to predict sgRNA efficacy for CRISPR-Cas9, improving gene-editing accuracy by 15%

Presented a poster session at the HDRUK conference on predictive modelling for CRISPR-Cas9 gene editing.

Delivered AWS SageMaker training to researchers, enhancing adoption of scalable data workflows and deployment within CRUK