Proprietary Research, Market Intelligence - TMT Sector. Confidential.

• Develop data science-driven investment insights and market signals for investor and institutional clients by leveraging alternative data, including both Citi proprietary data and data vendor partners• Technical lead in various data science projects: 1. Macroeconomic forecasting using Citi’s proprietary consumer credit card spend data, along with sector and industry-specific case studies 2. Employment and workforce analysis - Utilising web-scraped job postings data to forecast unemployment - Company-specific analysis – abrupt hiring pauses, geographical expansions - Skills demand and supply modelling to infer company strategies and benchmark against peer groups using NLP models (e.g. BERT) 3. Supply chain resilience analytics with graph data science - Dependency analysis and disruption forecasting using shipment data - Analysing centrality and concentration effects in companies’ supply chains - Modelling the ESG / climate / biodiversity impact of supply chains on companies and identify potential greenwashing issues 4. Earnings call transcript analysis for corporate clients using advanced NLP - Sentiment analysis, linguistic style analysis, topic modelling 5. Thematic basket creation process through a bespoke Machine Learning-based method of blending company revenue, patents, news and jobs data 6. Portfolio construction for hedge fund clients using ESG signals• Cross-departmental collaboration primarily with Equity Research analysts and also Citi’s treasury business, investment bank, consumer & private bank, Citi Ventures / innovations teams to maximise the utility of data science output• Led the architecture design and implementation of a new Data Science platform for the team to collaborate on across data science projects

• Anomaly detection and clustering analysis on global transactions data to identify fraud patterns• Improve client retention in Asset Management through customer profiling and churn analysis• Reduce costs associated with cash replenishment logistics across the Santander UK's network, using time series forecasting, clustering analysis and mathematical optimisation techniques• Reduced ATM downtime with anomaly scoring algorithm on ATM performance• Active contributor to the Data Science Community: inner sourcing, competitions and education programmes