I’m Faheem Jabbar, a Quantitative Data Analyst with 2+ years of experience in financial analytics, econometrics, and machine learning.
I specialize in Python, R, and Stata, building projects that bridge finance, analytics, and research — from algorithmic trading strategies and option pricing models to credit risk prediction, fraud detection, and financial forecasting.
Alongside finance, I’ve worked on analytics projects in policy, HR, and health, applying econometrics, ML, and statistical modeling to deliver insights.
My GitHub reflects my passion for modular, production-ready code and turning complex datasets into actionable outcomes.
- Backtesting & optimizing quantitative trading strategies (RSI, MACD, blended indicators)
- Credit risk & fraud detection models using Random Forest, XGBoost, and Neural Networks
- Automated data pipelines for finance & policy datasets
- Research reports that merge econometrics and financial modeling
- Remote-first data & analytics roles
- Quantitative finance collaborations
- Freelance research/consulting projects
- Contact Me At: faheemjabbar326@gmail.com
Lead Data Analyst | Assignlytic (2020 – Present)
- Delivered 500+ analytics projects across finance, econometrics & policy domains.
- Built automated pipelines for financial data sets and predictive model backtesting.
- Authored research reports and presented insights to international clients.
Research Analyst | Independent Consulting (2024 – 2025)
- Developed Python scripts for multi-source data integration.
- Conducted consulting research in risk assessment and forecasting.
- Produced reports summarizing findings and strategy implications for stakeholders.
Data Analyst Intern | Planning & Development Board (2023)
- Analyzed policy datasets with regression and forecasting models.
- Produced monitoring briefs and standardized reporting templates.