From Data to Alpha: Engineering the Future of Quantitative Finance

I architect and implement the quantitative systems that transform complex data into measurable market advantage. My work sits at the intersection of data science and financial engineering, building institutional-grade infrastructure that moves from research to production seamlessly.

My Journey

My fascination with markets began not just with price movements, but with the underlying data patterns that drive them. This curiosity led me to pursue dual expertise in Data Science and Financial Engineering at Washington University in St. Louis, where I've built the theoretical foundation for solving complex financial problems through quantitative methods.

During my internship at Amphora Investment Management, I saw firsthand how manual processes were limiting analytical capabilities. This experience solidified my focus on building automated systems that not only generate alpha but do so with efficiency and precision at institutional scale.

Data Engineering & Infrastructure

Python • SQL • APIs • Cloud Infrastructure

Architecting scalable data pipelines that integrate disparate sources, cutting manual processing by 80% and ensuring high-fidelity data for investment analytics and decision-making.

Algorithmic Systems Development

Low-Latency Architecture • WebSockets • Quantitative Models • C++

Engineering full-stack trading platforms from data ingestion through signal generation, achieving sub-second processing latency for high-frequency execution and alpha capture.

Quantitative Research & Modeling

Statistical Modeling & Inference • Time Series Analysis • Stochastic Calculus • Derivatives Pricing • Machine Learning • Probability Theory

Building sophisticated models for portfolio optimization, derivatives pricing, and alpha research using statistical inference and machine learning techniques to derive signal from market noise.

FAQ

What is your focus?

I specialize in quantitative modeling and algorithmic trading.

What technical skills?

Python, SQL, R, and machine learning libraries are my core tools.

Any hands-on projects?

I built automated Python pipelines cutting manual work by 80% and created a full-stack high-frequency crypto trading platform.

When can you start?

I'm available for full-time roles from May 2026 onward.

What roles do you seek?

Looking for quantitative research analyst or financial engineer positions.

How do you approach problem solving?

I focus on data-driven methods, leveraging statistical models and automation to optimize financial strategies efficiently.

Location

Based in St. Louis, I’m immersed in a vibrant academic and financial community that fuels my passion for quantitative finance.

Address

Washington University, St. Louis

Hours

Mon-Fri, 9am-5pm

Let's Build the Future of Finance

I'm always interested in connecting with professionals pushing the boundaries of quantitative finance and systematic trading. Whether you're working on innovative models, scalable infrastructure, or novel data approaches—let's discuss how we can engineer better market solutions.

Market Philosophy & Research

My curiosity extends beyond system architecture to the fundamental dynamics of markets:

• Exploring the theoretical and practical limits of machine learning in predicting market microstructure.

• Analyzing the historical evolution of liquidity and price formation across global asset classes.

• Investigating how unconventional data streams can challenge traditional quantitative assumptions.

• Engaging with the academic and open-source community to shape the next generation of financial tools.

Professional Philosophy

I believe that sustainable alpha generation requires three core components: rigorous quantitative research, robust engineering infrastructure, and continuous innovation. My approach combines academic rigor with practical implementation—transforming theoretical models into production-ready systems that deliver consistent performance.

The most effective quantitative solutions aren't just mathematically sound; they're engineered for reliability, scalability, and adaptability in dynamic market conditions.