Eesaa Gafar

Data Science undergraduate at the University of Nottingham, applying Python, statistical modelling and machine learning to large, noisy, real-world datasets. Combines a data science apprenticeship with quantitative research exposure to financial markets. Keen to discover, enrich and analyse data sources that drive research.

Data Science Apprentice - The Mercian Trust - Walsall, UK

(2024-09)

• Led a Trust-wide professional-growth initiative end-to-end: automated form collection via Power Automate to feed live Power BI dashboards with embedded row-level security (RLS), giving each school's staff a filtered split-view of only their own data. 500+ staff, 12 schools
• Built an end-to-end budget-reporting automation: two files dropped into SharePoint trigger a lookup that cross-references budget codes to holders, then automatically emails each budget holder their figures replacing a manual, error-prone process. 12 separate schools each with different budget lists/codes and emails.
• Cleaned, validated and modelled large volumes of student data in Python and Excel, and automated recurring API pulls with Power Automate to produce forecasts and insights that informed Leadership-team decisions Completely automated student report data exports to feed live dashboard.

Quantitative Research Intern - Level2 - Remote

(2024-07 - 2024-07)

• Developed and back-tested multiple systematic trading strategies on historical market data.
• Produced equity research reports analysing price movements, volatility, risk and competitive positioning across selected stocks.
• Built practical understanding of market indicators, patterns and the drivers of financial markets.

BSc Data Science (Degree Apprenticeship) - Data Science - University of Nottingham (2024-09 - 2027-09)