Alejandro Gonzalez

Statistically analyzed proprietary data on auction performance to create a report showcasing a discrepancy between our current marketing efforts and our art collectors. Recommendations based on my findings were implemented, resulting in an increased marketing presence. Data Analysis and plots were done using Python, Pandas, and Seaborn. Prepared client and company deliverables, findings, draft and final reports for a $5M contract. Liaisoned with UNHW clients to increase client relations for my team’s pre-sale reception event.

Streamlined reporting workflows using Python and SQL APIs, increasing operational efficiency by 15% in global regions. Resolved a data pipeline issue before affecting users with cross-functional teamwork. Optimized 160,000+ user performance KPIs globally, improving performance measurement accuracy by 9%. Created robust technical documentation before product launch, enabling 250+ stakeholders globally to make data-driven decisions.

Developed and simulated systematic trading strategies in Excel by optimizing capital allocation across limited backtesting scenarios to validate trading hypotheses. Achieved top 8% performance among 300 full-time traders. Applied statistical arbitrage techniques in live pit trading simulations, executing real-time position management based on market events.

Modeled probability spaces in the realm of non-Euclidean geometry: a geometry where lines are curved and distance gets exponentially bigger. With my advisor Dr.Albert Artilles (Now at Tsinghua university), I presented the connection between the poincaré disk and Möbius transformations, showing that one is the 2D projection of the other. Math involved: Complex Analysis, Real Analysis, Group Theory, Projective Geometry, and Topology.

Statistical models are fundamental to identify and understand can- cerous tendencies and properties in our bodies. Much of the current research focuses on the relationships between binary gene expressions and cancer incidence, which often leads to uninterpretable models due to complex relationships between gene expressions. Instead, using knot identification and analysis in nonlinear modeling creates more in- terpretable trends. Using data by age, sex, and race from the National Cancer Institute, we analyze leukemia incidence in the period 1975- 2017 using regression splines, a technique that partitions the model into several piecewise functions at various knots in the covariate space. Knot locations are chosen to provide interpretable results and mini- mize the least squared error, which allows for inference based on tech- niques from linear regression. We use the model to examine trends over time based on covariates and conjecture why relationships change at the knots, guiding future researchers on specic groups. 1

Selected as 1 of 40 students to participate in a multi-day fellowship program at the D. E. Shaw hedge fund. Analyzed seasonal mobility data reports by traders to explore strategies in climate finance and carbon market mechanisms. Explored the intersection of cognitive psychology and finance through in-depth case studies, workshops, and discussions.

Selected as one of 110 participants out of a highly competitive pool for UC Berkeley's Trading Competition, a 3-day event consisting of 5 trading events integrating game theory, statistics, and machine learning techniques with a $10,000 cash prize.

Selected as 1 of 50 students to understand the market-making industry through a multi-day program at Jane Street.

STEM internship focused on the biological expressions of genes, cancer cells, and DNA through experiments at the Fred Hutchinson training laboratory.