MD AMIR KHAN
Data Scientist | Data Engineer | Financial Engineer
A versatile data professional with expertise in quantitative finance, data science, and data engineering. I specialize in leveraging advanced analytics, machine learning, and robust data pipelines to solve complex business problems across financial markets, investment strategies, and enterprise data systems. With a strong foundation in statistical modeling, programming, and data architecture, I thrive on transforming raw data into actionable insights and scalable solutions.
About Me
I am a versatile data professional with a passion for transforming complex data into actionable insights and scalable solutions. My expertise spans quantitative finance, data science, and data engineering, enabling me to tackle diverse challenges across financial markets, enterprise systems, and analytical workflows. With a strong foundation in statistical modeling, programming, and data architecture, I excel at solving complex problems through innovative data-driven approaches.
In the realm of data science, I specialize in building predictive models, performing advanced statistical analysis, and developing machine learning solutions that drive business value. My experience includes end-to-end data science workflows from data collection and preprocessing to model deployment and monitoring. I have successfully implemented various ML algorithms including neural networks, ensemble methods, and time series forecasting models.
As a data engineer, I design and implement robust data pipelines, ETL processes, and data infrastructure that can handle large-scale, real-time data processing. I have extensive experience with cloud platforms, database optimization, and building scalable data architectures that support both analytical and operational workloads.
In quantitative finance, I have developed sophisticated trading algorithms, risk management systems, and portfolio optimization frameworks. My work combines traditional financial theory with modern data science techniques to create innovative solutions for investment management and risk assessment.
My strong analytical mindset and technical versatility enable me to work effectively across different domains, from financial markets to enterprise data systems. I am a collaborative team player who thrives in fast-paced environments, where I can leverage my diverse skill set to work closely with data scientists, engineers, traders, and business stakeholders.
I am constantly expanding my knowledge and staying current with the latest advancements in data science, machine learning, and data engineering. My core areas of expertise include:
- Machine Learning & AI: Neural networks, ensemble methods, deep learning, NLP
- Data Engineering: ETL pipelines, cloud platforms, database optimization, real-time processing
- Data Science: Predictive modeling, statistical analysis, feature engineering, model deployment
- Quantitative Finance: Portfolio optimization, risk modeling, algorithmic trading
- Big Data Technologies: Spark, Hadoop, Kafka, distributed computing
- Cloud & DevOps: Docker, CI/CD, infrastructure as code
3.90
Current GPA
5+
Years Experience
10+
Projects Completed
Education
Master's in Financial Engineering & Analytics
Stevens Institute of Technology
2024 - 2025GPA: 3.90/4.00
Focus: Quantitative Finance, Algorithmic Trading, Risk Analytics, Portfolio Optimization
Bachelor in Business Administration
North South University (NSU)
2018 - 2022Major: Finance | Minor: Mathematics
Key Courses: Calculus, Linear Algebra, Differential Equations, Corporate Finance, Investment Theory, Financial Derivatives, Applied Statistics
Latest News
Professional Certification
FRM Part I Exam Preparation
Currently preparing for the Financial Risk Manager (FRM) Part I examination, focusing on quantitative analysis, financial markets and products, and risk management foundations.
Research Paper
Towards a Robust PCA and Dynamic Factor Portfolios Updating
Working on a research paper with Professor Papa Momar NDIAYE. In this paper, we propose a dynamic tracking algorithm that modifies the classical Principal Component Analysis to reduce the instability of risk levels and principal factors. The goal is twofold: to ensure to the principal factors some immunity against perturbations on observations and to stabilize the factors when updating covariance matrix.
Those factors are of paramount importance in a portfolio allocation setting where they form a basis of orthogonal factor portfolios that generates the efficient frontier. By ensuring a smooth transition between those factors portfolio under some spectral conditions and detecting changes in risk clusters that warrant a reset of the entire tracking process, we provide tools to improve the timing of portfolio rebalancing and reduce transaction costs.
Experience
Data Science Research Assistant
Stevens Institute of Technology
June 2024 - Present ยท Part-time- Led end-to-end data science projects combining quantitative finance with advanced analytics, focusing on machine learning model development, data pipeline engineering, and automated trading systems
- Architected and implemented scalable data engineering solutions for processing large-scale financial datasets, including real-time data ingestion from Polygon API and Nasdaq auction data using Python, Apache Spark, and cloud platforms
- Developed and deployed production-ready machine learning models (neural networks, LightGBM, ensemble methods) for predictive analytics, achieving high accuracy in short-term price movement prediction and options pricing
- Built comprehensive backtesting frameworks and automated performance monitoring systems with real-time dashboards, enabling data-driven decision making and strategy optimization
- Designed robust data pipelines and ETL processes for high-frequency trading data, implementing automated feature engineering, model validation, and risk management systems
- Pioneered research in AI-powered financial applications, exploring agentic AI and financial LLM integration for next-generation trading frameworks and automated decision systems
Quantitative Data researcher
United Commercial Bank
January 2022 - December 2023- Engineered automated SQL/Python pipelines for 1,400+ funds, streamlining data processing and reporting workflows
- Built Tableau/Power BI dashboards for $200M+ portfolios, enabling managers to monitor volatility and downside risk
- Designed predictive allocation models, boosting Sharpe ratio by 34% through EWMA and shrinkage covariance methods.
- Applied statistical attribution methods to 14 quarters of fund data, uncovering performance drivers and enhancing compliance
- Delivered business-facing insights by translating complex data outputs into clear, actionable recommendations for executives
Quant Investment & Data Analyst
Standard Chartered Bank
January 2021 - December 2021- Built scalable Python/SQL pipelines to process 50K+ daily transactions, improving data integrity and anomaly detection
- Analyzed and backtested equity portfolios using pandas, matplotlib & Plotly, delivering performance dashboards (return, drawdown, risk metrics) to portfolio managers to evaluate strategy effectiveness.
- Automated daily risk and performance reporting for equity strategies with $200M+ AUM, ensuring accuracy and timeliness
- Designed Tableau dashboards to visualize key portfolio metrics, enabling senior management to track risk and performance
- Collaborated with portfolio and risk managers on $100M+ portfolios, translating analytics into actionable insights improving investment decisions
Projects
Bond Portfolio Optimization and Immunization
August 2025Comprehensive bond portfolio management system combining quantitative finance with data engineering. Implements duration matching, convexity adjustments, and immunization strategies using real-time data pipelines, automated risk calculations, and scalable portfolio optimization algorithms for fixed income portfolios.
Vasicek Bond Pricing Model - Monte Carlo, PDE & Analytical
July 2025Comprehensive implementation of the Vasicek interest rate model featuring three pricing approaches: analytical solutions, Monte Carlo simulations, and PDE finite difference methods for zero-coupon bonds.
Portfolio Optimization
July 2025Strategic asset allocation framework using modern portfolio theory, risk parity, and advanced optimization techniques with Riskfolio-Lib for multi-asset portfolio construction.
Vasicek Bond Pricing and Kalman Filtering
June 2025Multi-method fixed income modeling combining Vasicek interest rate dynamics with Kalman filtering for parameter estimation and state variable tracking in bond pricing applications.
Data Science Projects
June 2025Collection of data science applications in finance including statistical analysis, machine learning models, and data visualization for financial time series and market data.
Trading Strategy Based on MACD Signals
June 2025Technical analysis-driven trading strategy using MACD (Moving Average Convergence Divergence) indicators for signal generation, backtesting, and performance evaluation.
Cryptocurrency Forecasting Using ARIMA
June 2025Time series forecasting application for cryptocurrency price prediction using ARIMA models, stationarity testing, and model selection for optimal forecasting accuracy.
Stock Price Prediction and Trading Strategy Using LSTM
March 2025Deep learning approach to stock price prediction using LSTM neural networks, combined with algorithmic trading strategy development and performance backtesting.
Stock Brokerage System Low Level Design
February 2025High-performance stock brokerage system architecture featuring order matching engine, portfolio management, and real-time market data processing.
Option Pricing Models
February 2025Comprehensive options pricing library implementing Black-Scholes, binomial trees, and Monte Carlo methods for European and American options valuation with Greeks calculation.
SPY Momentum Alpha Backtesting
February 2025High-frequency momentum trading strategy combining data engineering and quantitative finance. Built robust data pipelines processing 2 years of SPY tick data from Polygon API, implemented real-time signal generation, and achieved 79% total return with comprehensive performance analytics and automated backtesting frameworks.
Pairs Trading Strategy
February 2025Statistical arbitrage strategy using cointegration analysis and mean reversion. Employed Euclidean distance method for pair selection with z-score based entry/exit signals.
Options Pricing Using Machine Learning
September 2024Advanced machine learning approach to options pricing combining deep learning with financial engineering. Implemented neural networks, random forests, and ensemble methods with automated feature engineering, model validation pipelines, and real-time pricing systems that outperformed traditional Black-Scholes pricing in complex market conditions.
Market Analytics Web Application
August 2024Full-stack data science application for comprehensive market analysis. Built interactive web application with real-time data ingestion pipelines, advanced data visualization dashboards, automated technical indicator calculations, and machine learning-powered trading signal generation with scalable cloud deployment.
Activities & Awards
Student Membership
CFA Society New York
Student member, actively engaged in professional events
Open Source Contribution
Riskfolio-Lib
Contributing to a leading Python library for portfolio optimization and risk management
Competition Participation
WorldQuant's 2023 International Quant Championship
Competed in crafting & testing advanced trading strategies
Certifications & Licenses
Complete Algorithmic Trading Course with Python, ChatGPT, ML
Udemy
July 2025Comprehensive algorithmic trading course covering Python programming, machine learning integration, and ChatGPT applications for automated trading strategies.
Akuna Capital Options 101
Akuna Capital
July 2025Professional options trading course from leading market maker covering payoff diagrams, volatility, Greeks, and market-making fundamentals.
Complete Data Science, Machine Learning, DL NLP Bootcamp
Udemy
July 2025Comprehensive bootcamp covering data science fundamentals, machine learning algorithms, deep learning, and natural language processing applications.
Taking Python to Production: Professional Onboarding Guide
Udemy
July 2025Advanced Python course focusing on production deployment, best practices, and professional development workflows for enterprise applications.
The Ultimate JSON With Python Course + JSONSchema & JSONPath
Udemy
July 2025Comprehensive JSON handling in Python including schema validation, path queries, and advanced data manipulation techniques.
Master Time Series Analysis and Forecasting with Python 2025
Udemy
June 2025Advanced time series analysis covering ARIMA, SARIMA, Prophet, LSTM, and modern forecasting techniques for financial and business applications.
Manage Finance Data with Python & Pandas: Unique Masterclass
Udemy
July 2025Specialized course on financial data management and analysis using Python and Pandas for quantitative finance applications.
Master Regression & Prediction with Pandas and Python [2025]
Udemy
July 2025Advanced regression analysis and prediction modeling using Python and Pandas for financial and statistical applications.
Mathematics-Basics to Advanced for Data Science And GenAI
Udemy
July 2025Comprehensive mathematics foundation covering linear algebra, calculus, probability, and statistics for data science and AI applications.
Python Object Oriented Programming (OOP): Beginner to Pro
Udemy
July 2025Advanced Python OOP concepts including inheritance, polymorphism, design patterns, and enterprise-level programming practices.
The Complete SQL Bootcamp (30 Hours): Go from Zero to Hero
Udemy
July 2025Comprehensive SQL training covering database design, complex queries, optimization, and real-world database management scenarios.
Fixed Income Analytics: Pricing and Risk Management
Udemy
July 2025Specialized fixed income course covering bond pricing, yield curve analysis, duration, convexity, and interest rate risk management.
Learn Python Requests
Udemy
July 2025Specialized Python course focusing on HTTP requests, API integration, and web scraping for financial data collection.
The Ultimate Pandas Bootcamp: Advanced Python Data Analysis
Udemy
July 2025Advanced Pandas mastery for complex data manipulation, analysis, and visualization in financial and business contexts.
FastAPI - The Complete Course 2025 (Beginner + Advanced)
Udemy
July 2025Modern Python web framework for building high-performance APIs, essential for financial data services and algorithmic trading platforms.
Mathematical Foundations of Machine Learning
Udemy
2025Deep mathematical foundations covering linear algebra, partial derivatives, calculus, and probability theory for advanced machine learning applications.
Python Data Analysis: NumPy & Pandas Masterclass
Udemy
2025Advanced data analysis techniques using NumPy and Pandas for quantitative finance and statistical computing applications.
GSX Verified Certificate for Probability - The Science of Uncertainty and Data
MIT / edX
December 2022Rigorous probability theory course covering uncertainty quantification, statistical inference, and data analysis fundamentals from MIT.
Python and Statistics for Financial Analysis
Coursera
February 2022Specialized course combining Python programming with statistical methods for financial data analysis and investment decision making.
Technical Skills
Programming Languages
Data Science & ML Libraries
Data Engineering & Big Data
Cloud & DevOps
Development & Tools
Data Science & Analytics
Quantitative Finance
Resume
MD Amir Khan - Resume
Data Scientist & Financial Engineer
Updated: December 2024
Format: PDF
Get In Touch
mkhan37@stevens.edu
Phone
+1 (201) 234-7017
Location
Hoboken, New Jersey, USA
linkedin.com/in/amirkhan2317
Portfolio
Get In Touch
I'd love to hear from you! Please feel free to reach out through any of the following methods:
Email: mkhan37@stevens.edu
Phone: +1 (201) 234-7017
Location: Hoboken, New Jersey, USA