Dmitri De Freitas

Engineering data pipelines and ML systems for quantitative finance

Python, Databases, Excel

Showcasing hands-on quantitative finance and data science work

Data Pipeline

Automated data processing reducing manual work by 80%

Portfolio Opt

Applied quantitative models to optimize asset allocation

Trading Infrastructure

Built a sub-second latency crypto trading system

Time Series

Analyzed financial trends using machine learning

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Experience Matters

End-to-end builder of quantitative systems: architecting automated data pipelines that cut manual processing by 80%, engineering high-frequency trading platforms with sub-second latency, and applying statistical modeling to derive actionable insights from complex datasets.

Technical Expertise

• Python

• SQL

• R Studio

• Scikit-learn

• VSCode

• Matplotlib

• Seaborn

• Excel/VBA

• PowerBI

• Tablaeu

• Quantlib

• MATLAB

• Bloomberg

• FRED

• Git

Transforming complex data into high-performance financial solutions.

Quantitative Modeling

Construction of sophisticated models for portfolio optimization and derivatives pricing using statistical techniques and machine learning.

Algorithmic Trading

Architecture of low-latency trading platforms that process real-time data for high-frequency execution with sub-second latency.

Data Engineering

Development of automated pipelines that integrate disparate sources, cutting manual processing by 80% and providing clean, reliable data.

Dmitri's quantitative models cut our analysis time in half by automating complex workflows, delivering immediate value and impressive results.

J. Lee

★★★★★

Much thanks

★★★★★

I have encountered a handful of students who possess both the aptitude and the genuine interest to be successful in this industry.

Talented Student

Adjunct Professor

John M. Nolan

Drew University

Dmitri is one such student.

Python, APIs

WebSockets, HTML