• Trade diversified portfolio of global and sector equity ETFs and futures.• Research and implement trade signals across multiple timeframes that leverage market microstructure, technical analysis, fundamentals, macro data, and more.• Use ML tools for precise timing of trades, sizing positions, and optimizing hyper-parameters for live trading.• Build trade signal files and reporting system to monitor real-time exposure.• Allow users to deploy strategy on their respective brokerage accounts via AWS.

• Drafted RRP methodology documents for cash flow models and balance sheet impacts for various credit products.• Worked with IT to implement cash flow models across various levels of business and product segmentation.• Worked with validation for supporting the model methodology, code and results in line with model assumptions.

The Global Algorithmic Institute (Global A.I.) is a think-tank devoted to the research, development and implementation of intelligent algorithms that leverage big data techniques and statistical machine learning techniques to foment the stability of the global financial markets and reduce the systemic risk arising from both HFT and the increasing electronification of Fixed Income and derivatives markets.

• Predicted losses under stressed macro scenarios across various levels of business segmentation for Americas, EMEA, and Asia. • Used SQL to pull and aggregate loan-level data to feed PD/LGD models and generate loss attribution reports.• Applied Markov models to predict obligor credit rating migrations for each country and time horizon.

Coursework: Mathematical Finance, Machine Learning, Statistical Inference, Time Series Analysis, Stochastic Processes, Quantitative Risk and Portfolio Management

• Researched and built dimension reduction algorithms (e.g. PCA, Random Matrix Theory, etc.) to decompose risk.

• Analyzed money manager transactions and created risk scores based on performance.

• Researched and built systematic intraday strategies for FX and Futures with long-term event-driven rebalancing.• Designed automated reports to reconcile PnL and monitor exposure across each instrument, sector and strategy.

• Recovered over $1.2B in mortgage putbacks by designing real-time database to monitor RMBS at various levels of attribution. • Analyzed terms of legal documents to determine grounds for mortgage repurchase.• Used Machine Learning methods to build customized mortgage prepayment model.

• Reconciled multiple databases of employee benefit data using Access and SQL databases.• Designed and implemented a methodology to calculate accrued benefits.• Revised and updated Schedule B disclosure statements.