seeking summer 2026 internships

Building software where
code meets capital.

CS student shipping trading tools, risk dashboards, and market data pipelines.

location New York, NY
education CSI '27 • 3.8 GPA
focus quant • fintech

Background

I build software at the intersection of finance and technology. My projects range from AI-powered stock forecasting (15% accuracy improvement over baseline) to algorithmic trading bots with 60% win rates across 500+ backtested trades.

Currently a CS student at CSI (Class of 2027) with a 3.8 GPA, I've shipped full-stack applications, real-time data dashboards, and quantitative trading systems—all from idea to working code in days, not months. My work combines clean Python backends, React frontends, and a deep interest in how markets actually move.

Outside of class, I'm a part-time caregiver for my grandmother, which has taught me patience, consistency, and how to stay composed when systems break—skills that translate directly to debugging production code and working under pressure.

What I'm looking for

Summer 2026 SWE or Quant Research internships at trading firms, hedge funds, or fintech companies. I want to work on teams that ship fast, value data-driven decisions, and let juniors touch production systems.

Interested in: HFT infrastructure, risk management systems, market data platforms, backtesting engines.

P.S. I collect fragrances and can deadlift twice my bodyweight. Not on my resume, but context matters.

Languages & Frameworks

  • Python — Strong
  • JavaScript/TypeScript — Strong
  • SQL — Proficient
  • Java, C++, Go — Familiar

Finance & Quantitative

  • Financial modeling
  • Algorithmic trading
  • Risk management
  • Time series forecasting
  • Pandas, NumPy, Prophet

Infrastructure & Tools

  • React, Node.js, Flask
  • PostgreSQL, MongoDB
  • Docker, Git, REST APIs
  • WebSocket, real-time data

Selected Work

AI + Finance
↑ 15% accuracy

StockVision AI

Built stock prediction platform that processes time series data and forecasts price movements using Facebook's Prophet algorithm.

What Changed:

  • 15% accuracy improvement over baseline ARIMA models in 30-day forecasts
  • Real-time processing for 50+ concurrent users with <200ms API response time
  • Interactive visualizations with TypeScript/React frontend and Flask backend
Python Flask Prophet React TypeScript
Algo Trading
60% win rate 2:1 R:R

Smart Money Trading Bot

Algorithmic trading system using price action, order flow, and smart money concepts to identify high-probability trade setups.

What Changed:

  • 60% win rate across 500+ backtested trades on EUR/USD and S&P 500 futures
  • Consistent 2:1 risk-reward with dynamic position sizing based on volatility
  • Real-time signal generation via TradingView webhooks with <5s execution latency
Python TradingView API Pandas Risk Management
Data Visualization
10K+ data points

F1 Racing Dashboard

Real-time Formula 1 analytics platform that processes telemetry data and race statistics through WebSocket connections.

What Changed:

  • 10,000+ data points per session with optimized rendering at 60fps on mobile
  • WebSocket integration for live telemetry updates with <100ms latency
  • Responsive design supporting desktop, tablet, and mobile viewports
React Chart.js D3.js WebSocket
Full Stack
100+ users

Smoke & Scent

Marketplace platform for collectors with enterprise-grade authentication and real-time communication features.

What Changed:

  • Enterprise authentication with JWT and 2FA supporting secure user sessions
  • Real-time chat system via Socket.io handling 100+ concurrent users
  • Normalized PostgreSQL schema with comprehensive audit logging and ACID compliance
React Node.js PostgreSQL Socket.io JWT