Conducted factor research across fixed income, equity, and MBS asset classes, leveraging alternative datasets, clustering techniques, and machine learning models including XGBoost and LSTM to capture non-linear risk dynamics. Applied Bayesian modeling and model validation frameworks to assess near-term physical climate risks and transition risk exposures, integrating catastrophe modeling and delinquency analysis into risk-adjusted metrics. Developed and validated quantitative models using Sharpe ratio-based evaluation to identify transition opportunities and assess climate-adjusted performance across multi-asset portfolios.

Specialized in risk modeling and pricing across fixed income, MBS, and equity asset classes, with expertise in risk decomposition, Value-at-Risk (VaR), and scenario-based stress testing. Applied Monte Carlo simulation, GARCH-based volatility modeling, and time series analysis to quantify credit and market risk across multi-asset portfolios. Developed CVaR measures and climate-adjusted credit risk estimates (PD/LGD), integrating macroeconomic factor models and PCA-driven risk decomposition into stress testing and scenario analysis frameworks.

Worked on deep research projects related to transition risks for firms, developing methodologies, producing prototype models, and scaling these with Python for public companies using NGFS scenarios. Projects involved creating a bespoke cost pass-through methodology, calculating cost of equity, and modeling capital expenditure over time. Collaborated with a multidisciplinary team of engineers and economists. Integrated into the tech stack using cloud computing, data pipeline orchestration, and data warehousing to build scalable solutions.

Priced technological risk in the atomic energy industry using exogenous shocks and a mathematical model to demonstrate the impact of technology complexity and investment costs on atomic energy stock prices. Developed a novel text-based political sentiment measure using NLP algorithms from presidential communications to price a cross-section of asset classes including equities, treasuries, swaps, options, and commodities. Awarded an £80,000 research grant in recognition of exceptional academic achievements and potential to contribute significantly to academia.

Spearheaded proprietary searches and leveraged industry connections to identify high-value investment opportunities in fintech and healthcare, collectively valued at £17 million. Streamlined financial modeling and valuation processes using DCF, IRR, and LBO techniques, significantly reducing lead time and accelerating takeover bid evaluation. Developed an end-to-end deal sourcing and evaluation process, resulting in a notable increase in potential acquisition targets.

Research Associate at EPoD India at IFMR, a joint program by Evidence for Policy Design at Harvard Kennedy School and the Institute for Financial Management and Research. Established data infrastructure and optimized processes through coding best practices, significantly reducing data errors. Implemented policies to stimulate knowledge creation for MSMEs in developing regions. Designed policy briefs for government partners and utilized Python to create visualizations empowering policy audiences to understand complex data patterns.

Researched and analyzed the UK investment landscape, identifying emerging trends and conducting competitive analysis that modeled a 15% market share increase within three years. Pioneered a £500m investment fund's entry into the UK market through a comprehensive proposal. Developed an actionable investment strategy with portfolio recommendations for risk mitigation and return maximization.