Conducted research and reviewed various public ESG data sources and applied web scraping techniques to gather relevant data, allowing for accurate measurements of sustainability performance.

Created PowerBI dashboards to analyse sustainability ESG performance, and identified correlations in the database to reduce the dimensionality of the data.

Forecasted ESG scores using historical ESG data through ESG performance analysis and generated recommendations for various products based on the computed data.

Developed and implemented machine learning models to extract insights and patterns from datasets, using statistical analysis and programming languages such as Python.

Automated ESG data collection and analysis systems using web scraping and data processing tools, and deployed these systems to scrape large amounts of ESG data from public sources, structuring it for machine learning analysis.

Designed and executed experiments to evaluate the effectiveness of machine learning models and algorithms, using appropriate evaluation metrics and statistical tests to determine their performance.

Collaborated with cross-functional teams to develop and deploy machine learning solutions that solved business problems, leveraging interpersonal skills to communicate findings and recommendations effectively.

Optimised machine learning models for performance, scalability, and maintainability, using techniques such as hyperparameter tuning, ensemble methods, and deployment strategies.