Faizan Khan
Software Engineer → Quantitative Finance
CS student at Brooklyn College, finishing up in May 2026. I got deep into machine learning and ended up pointed at quantitative finance, where the engineering has to be as rigorous as the math.
Background
About Me
The Honest Version
My background is software engineering. That means when I'm working on an ML problem, I'm thinking about the pipeline, the API, what happens when the model drifts, and how you actually retrain it. Not just getting the accuracy number up in a notebook.
I ended up pointed at quantitative finance because it's one of the few places where both sides matter equally. You need to understand the math well enough to trust your signals, and you need to engineer well enough that the system actually runs in production.
I'm not trying to do everything. I want to work where the model and the system both have to be right, and figure out the hard parts of that in an environment where it actually matters.
Profile
Point of View
"Most quant researchers can't ship a production system. Most engineers don't really understand what the model is doing. I'm trying to be the person who does both, and so far that bet is paying off."
Background
Education & Experience
Education
CUNY Brooklyn College
Brooklyn, NY
- Relevant coursework: Data Structures, Analysis of Algorithms, Data Tools & Algorithms, Machine Learning
- Minor in Data Science, bridging statistical modeling with production engineering
Experience
Brooklyn College
Brooklyn, NY
- Engineered a production ML pipeline for NERIS (a federal firefighter reporting system) using the Anthropic Claude API and fine-tuned models to extract structured data from unstructured narratives at scale
- Built Python ETL pipelines with automated validation, schema enforcement, and anomaly detection, cutting manual processing time by 40% and reducing data quality incidents by ~30%
InZone Inc.
Remote
- Owned end-to-end delivery of a distributed microservices backend in Node.js/Express serving 5,000+ daily active users, leading architecture decisions from system design through production monitoring
- Containerized multi-service applications with Docker and GCP achieving 99.9% uptime; mentored 4 engineers on API design and coding standards
AutoLake LLC
San Francisco, CA (Remote)
- Drove a 25% increase in system throughput at a B2B data lake infrastructure company by standardizing RESTful API contracts across engineering teams
- Identified and remediated 15+ critical security vulnerabilities (XSS, CSRF) through systematic OWASP-aligned production audits
Scaled active membership to 100+ students; coordinated technical workshops on AI engineering, full-stack development, and systems design.
Portfolio
FinTech Projects
Three projects where I tried to build the real thing, not just the notebook version.
Quantitative Trading Framework
I wanted to know if momentum and mean-reversion signals actually work when you test them honestly: real transaction costs, out-of-sample periods, no cherry-picking. This is that experiment.
Credit Risk Scoring Engine
I built this to understand how banks actually score loan applications. Raw loan data, XGBoost model, a deployed REST API, and SHAP explanations for why each decision was made. The whole stack, not just the model.
Real-Time Fraud Detection
The interesting constraint here: blocking a real customer costs almost as much as missing actual fraud. I built the system around that tradeoff rather than just chasing recall.
Capabilities
Technical Skills
Machine Learning
- XGBoost / LightGBM
- SHAP Explainability
- Scikit-learn
- Imbalanced Learning (SMOTE)
- MLflow Experiment Tracking
- ROC-AUC / KS-Statistic
- Feature Engineering
Data Engineering
- Apache Kafka
- Redis Feature Store
- PostgreSQL / TimescaleDB
- ETL Pipelines
- REST API Data Scraping
- SQLite Time-Series Store
- Data Ingestion & Scheduling
FinTech Domain
- Credit Risk & Scorecard Design
- Fraud Detection Patterns
- Strategy Backtesting
- Portfolio Optimization
- Sharpe / Sortino / Calmar
- Basel III Framing
- Walk-Forward Validation
Cloud & DevOps
- AWS (Lambda, ECR, RDS)
- Docker / Docker Compose
- FastAPI
- GitHub Actions CI/CD
- Grafana Dashboards
- Plotly Dash
- Vercel / Render Deployment
Right Now
Quantitative Trading Framework: backtesting momentum + mean reversion signals across 500+ S&P 500 tickers with walk-forward validation
"Advances in Financial Machine Learning" by Marcos López de Prado (specifically the chapter on backtesting pitfalls)
How production ETL patterns from my research work translate to real-time market data pipelines: TimescaleDB vs. kdb+
Let's Connect
Get In Touch
I'm finishing my degree in May 2026 and actively looking for roles in FinTech and quant finance. If something here resonates, drop me a message. Or if you just want to talk horror films, that works too.
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