I'm a CFA/CPA working for a hedge fund in San Francisco and the Grand Cayman. My expertise is primarily in global macroeconomics, equity, FX, rates, and credit markets. My background spans long/short equity stock selection and more sophisticated hedge fund strategies, including the use of derivatives and structured products. I have developed several systematic trading strategies utilizing my proprietary algorithms that consistently outperform "buy-and-hold" or "indexing" strategies, on almost any market index, with far less beta exposure. My algorithms are designed to limit beta exposure, having you invested (or "in-the-market") only ~25% of the time and still outperform by 750%-1200%+ (over the full, 20-year backtest period -- that is cumulative outperformance). I have recently started to combined data science, largely computer programming (Python, R) with statistics and mathematics to identify & transform alternative data sets into predictive algorithms - this offers enormous potential to enhance my capabilities as a research analyst. While these skills can be used to add value in a variety of ways, I see tremendous opportunity in the integration of unstructured and structured data sets into formats analyzable with programs such as Python to obtain an informational advantage ahead of key equity (earnings) and macroeconomic releases (CPI, USDA). Machine learning and neural networks are central to this process, and as the landscape evolves I expect to integrate more advanced AI techniques. I am currently a graduate student (part-time) at Harvard (Data Science), building a stronger background in AI/ML.