Verified Expert in Engineering
Financial Markets Developer
Microsoft SQL Server, PyCharm, Windows
The most amazing...
...things I've developed were the trading strategies underpinning the hedge fund I ran and a retail gasoline price optimizer.
Principal Data Scientist
- Created software products that deliver advanced insights into their businesses, predicting sales based upon factors such as weather, seasonality, and demographics.
- Applied machine learning techniques for predictive purposes; techniques include regression/correlation, logit models, random forest models, clustering algorithms, lead/lag analysis, some NLP, multithreading, and multiprocessing.
- Optimized client retail prices based upon price elasticities and competitor behavior. Recursively propagated potential price changes throughout the entire client/competitor network and determined optimal price.
Co-Chief Investment Officer
EVA Capital Management
- Served as the co-CIO of a quantitative asset management firm. With a business partner, laid the groundwork for company operations, hiring, and trading.
- Pitched business plan and trading strategy to potential clients and business partners and raised seed capital.
- Led the researcher and portfolio manager responsible for daily R&D and trading on a systematic EVA (economic value added)-based stock selection strategy. Managed one analyst. Implemented in both hedge fund and index/ETF formats.
- Maintained terabytes of data in a SQL database which served a C++ trading algorithm to optimize the portfolio; a C# desktop app for data manipulation; and Python, MATLAB, and R scripts for statistical analyses.
- Utilized finance frameworks such as Fama-Macbeth regression, mean-variance optimization, multifactor risk modeling, as well as ML such as NLP and clustering.
- Coded the company web site using Node.js, Angular, and Highcharts.
Head of Quantitative Research
- Hire as the fifth employee at a financial company startup based around the Economic Value Added (EVA) valuation framework. Instrumental in growing revenues to over $7 million and employees to over 20.
- Created and marketed equity research reports, data visualizations, and interactive web tools to buy- and sell-side clients. Mined proprietary databases and tied statistical observations to non-technical, actionable investment recommendations.
- Researched finding areas of under/overvaluation, commenting on trends in market or factor behavior, and custom client projects.
- Led the research on many quantitative financial models, including an EVA-based global stock selection system and simulated ETFs designed to capture premia (beta) on various factors.
- Aggregated a DCF analysis, a custom-built portfolio analysis, risk, and attribution system; cost of capital models; and thematic models tying factor returns and exposures to the business and company life-cycles.
The Rohatyn Group
- Contributed as an analyst at a multi-billion dollar emerging markets hedge fund.
- Provided support for an earnings-estimates based trading model. Created a 100GB store of data; researched underlying data quality and effectiveness of various trading signals/indicators; backtested. Implemented in the master fund at $50 million.
- Created a cointegration-based pairs trading model and presented results to the trading desk weekly.
- Created a multi-factor model to forecast US corporate bond spreads utilizing equity market information.
- Presented results to the entire fixed-income research division for discussion.
- Received internship credit for my master's in financial engineering program at Berkeley.
Nomura Securities International
- Worked for the quantitative unit in an internal fund-of-funds.
- Created a custom performance attribution system (Brinson-based).
- Automated the pricing of options in the portfolio.
Predictive Maintenance for Printers
Long and Short Equity Hedge Fund
Maintained TBs of data in a SQL database, which served a C++ trading algorithm to optimize the portfolio, a C# desktop app for data manipulation, Python, MATLAB, and R scripts for statistical analyses. Techniques utilized finance frameworks such as Fama-Macbeth regression, mean-variance optimization, multifactor risk modeling, and ML such as NLP and clustering.
Retail Gasoline Price Optimizer
Python, SQL, C#, R, C++, Java, C#.NET
Data Science, ETL
Amazon Web Services (AWS), Windows, Azure
SQL Server 2014, PostgreSQL, MySQL, Elasticsearch, Microsoft SQL Server, SQL Server Integration Services (SSIS)
Machine Learning, Financial Markets, Risk Models, Data Warehouse Design, Natural Language Processing (NLP), Deep Learning, Oil & Gas, Artificial Intelligence (AI), GPT, Generative Pre-trained Transformers (GPT), Option Pricing
Master's Degree in Financial Engineering
University of California at Berkeley/Haas School of Business - Berkeley, CA
Bachelor's Degree in Chemical Engineering
Cornell University - Ithaca, NY
Chartered Financial Analyst