Quantitative Financial Analyst

Engineering
Alpha Generation
Through Data.

I architect low-latency algorithmic trading systems and robust risk modeling frameworks, bridging the gap between stochastic calculus and high-performance full-stack engineering.

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Career Progression

Professional Experience

2021 — Present

VP Quantitative Analytics

Nexus Capital Management | New York
  • Engineered highly concurrent, low-latency execution algorithms in C++ and Python, improving overall trade execution efficiency by 18.5% across global equities.
  • Architected robust risk modeling frameworks utilizing Monte Carlo simulations and stochastic calculus, reducing portfolio Value-at-Risk (VaR) estimation errors by 22%.
  • Led a cross-functional squad of 5 data scientists to deploy a real-time sentiment analysis pipeline ingesting 1M+ daily alternative data points.
  • Optimized legacy database queries (PostgreSQL/TimescaleDB), reducing data retrieval times for backtesting environments by over 60%.
2018 — 2021

Senior Financial Data Scientist

QuantCore Strategies | London
  • Developed predictive machine learning models (XGBoost, LSTMs) for short-term asset pricing anomaly detection, yielding a Sharpe ratio improvement of 0.4 in the proprietary trading desk.
  • Built a comprehensive internal web dashboard (React, Node.js, WebSockets) allowing portfolio managers to visualize real-time liquidity heatmaps.
  • Implemented automated ETL pipelines processing terabytes of tick-level order book data on AWS (S3, EMR, Spark).
2016 — 2018

Investment Banking Analyst (Quant Group)

Goldman Sachs | New York
  • Constructed automated pitchbook generation tools using Python and VBA, saving analysts an estimated 40 hours per week in manual formatting.
  • Assisted in the structuring of complex exotic derivatives by building pricing models utilizing Black-Scholes and local volatility models.
  • Conducted rigorous quantitative due diligence on M&A targets in the FinTech sector, analyzing tech stack scalability and unit economics.
Technical Proficiency

Tech Stack Radar

Quantitative Engineering

Python (Pandas, NumPy, SciPy) 95%
C++ (Low Latency Systems) 80%
Machine Learning (TensorFlow/PyTorch) 85%
Stochastic Calculus & Options Pricing 90%

Full-Stack Architecture

React.js / Next.js 88%
Node.js / Express 85%
PostgreSQL / TimescaleDB 92%
AWS (EC2, Lambda, S3, RDS) 80%

Methodologies & Soft Skills

Beyond code, I focus on delivering robust business value through structured methodologies and effective communication with stakeholders.

Agile/Scrum Leadership
Cross-functional Communication
Stakeholder Management
Risk Mitigation Strategy
CI/CD Pipelines
Microservices Architecture
Test-Driven Development (TDD)
Execution & Delivery

Selected Case Studies

Abstract trading dashboard visualization

Algorithmic Trading Engine

Architected a proprietary event-driven trading bot capable of processing market data via FIX protocol. Implemented statistical arbitrage strategies evaluating cointegrated asset pairs in real-time, achieving robust alpha during volatile market conditions.

Python C++ ZeroMQ FIX Protocol
Data analytics charts on screen

Dynamic Portfolio Optimizer

Developed a full-stack SaaS application for institutional wealth managers. The tool ingests client risk profiles and utilizes Markowitz Mean-Variance Optimization and Black-Litterman models to auto-generate suggested asset allocations.

React.js Node.js SciPy Docker
Stock market ticker glowing in dark

Market Sentiment Analyzer

Created a natural language processing pipeline that scrapes financial news APIs and Twitter firehose. Utilizing FinBERT, the system scores macroeconomic sentiment and feeds signals into a TimescaleDB for backtesting momentum strategies.

NLP / HuggingFace AWS Lambda TimescaleDB Apache Kafka
Server room glowing blue

Enterprise Risk Dashboard

Led the front-end modernization of a legacy banking risk system. Built a highly performant React SPA using WebGL for rendering complex 3D volatility surfaces and D3.js for interactive historical VaR simulations, processing 50k+ data points instantly.

React & Redux D3.js WebGL WebSockets
Academic Background

Education & Certifications

Master of Science in Financial Engineering

Columbia University | GPA: 3.9/4.0

Coursework: Stochastic Methods in Finance, Continuous Time Models, Optimization Methods, Machine Learning for Financial Data.

Chartered Financial Analyst (CFA)

CFA Institute | Charterholder since 2019

Demonstrated expertise in portfolio management, ethical standards, quantitative methods, and complex derivative valuation.

B.S. in Computer Science & Mathematics

University of Michigan | Cum Laude
Endorsements

Professional Recommendations

"Julian possesses a rare hybrid skill set. He deeply understands the complex mathematics behind our derivatives models, but can also roll up his sleeves and architect the scalable, low-latency infrastructure required to deploy them in production. A massive asset to any quant team."

Sarah Jenkins
Managing Director, Nexus Capital Management

"Working with Julian was transformative for our data pipeline. He led the transition from legacy batch processing to real-time streaming, reducing our time-to-insight from hours to milliseconds. His technical rigor and leadership are exceptional."

David Chen
Chief Technology Officer, QuantCore Strategies
Let's Connect

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Currently open for new opportunities, contract missions, or technical consulting. Send a message to discuss your quantitative infrastructure needs.

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