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Experience

Senior Quantitative Strategist with 4+ years of experience in financial modeling, machine learning, and full-stack development. Specializing in residential mortgage analytics, predictive modeling, and large-scale data processing systems.

Professional Summary

Masters in Statistics from the Indian Statistical Institute with specialization in Probability Theory and Financial Mathematics. Currently leading strategic analytics initiatives at Morgan Stanley's Fixed Income Division, developing cutting-edge predictive models for residential mortgage portfolios worth billions of dollars.

Expert in quantitative modeling, machine learning, and full-stack development with proven track record of delivering high-impact solutions that enhance forecasting accuracy by 10-15% and reduce processing time by 80-90%.

4+ Years Experience
25M+ Records Processed
20+ ML Models Deployed
10+ Technologies Mastered

Professional Journey

Senior Quantitative Strategist

Morgan Stanley, Fixed Income Division

Mumbai, India

July 2022 - Present

Leading strategic analytics initiatives for residential mortgage portfolios, developing sophisticated predictive models and analytics platforms that serve multiple trading desks and drive critical business decisions.

Key Projects & Achievements

USA Home Price Index Analytics & MSA Ranker
  • Built comprehensive tracker for USA Housing Market using Plotly Dash
  • Integrated 5+ vendor data sources with kdb+ and web-scraped FRED data
  • Developed Metro-area Ranking System using ARIMA/XGBoost hybrid models
  • Achieved ~10% reduction in MSE for 12-month HPA forecasts
  • Deployed Voting Regression on 8 hyperparameter-tuned models
In-House Repeat Sales Transaction Index
  • Automated Monthly Index Builder for 25+ million property transactions
  • Implemented Case-Shiller methodology with custom optimizations
  • Reduced processing time from 6+ hours to ~50 minutes
  • Enabled 2-months earlier pre-fetch estimates for actual HPI
  • Added custom index creation with hierarchical clustering
Transition Models for PD, LGD, EAD
  • Developed exhaustive feature selection using multi-threading
  • Processed 60+ million loan records with parallel chunk processing
  • Reduced feature selection time by 4-5 hours
  • Implemented Survival Analysis for Non-QM portfolios
  • Improved AUC by 0.12-0.17 vs legacy models
Hedge Optimizer & Scenario Analysis
  • Built Hedge Tool with Python and kdb+ integration
  • Parsed data from INTEX and multiple internal sources
  • Developed PyQt desktop app for scenario analysis
  • Enabled custom scenario creation capabilities
  • Optimized hedging strategies for selected portfolios
ResiDash & GLendA Development
  • Led development of Plotly-Dash internal analytics website
  • Built React-based Global Lending App (GLendA)
  • Implemented widget-based architecture for private side
  • Maintained both public and private side applications
  • Served multiple trading desks with complex analytics

Summer Associate

Morgan Stanley, Fixed Income Division

Mumbai, India

May - July 2021

Developed advanced machine learning models for American home price prediction, implementing comprehensive data processing pipelines and creating robust R-kdb+ frameworks for model deployment.

Key Projects & Achievements

ANN Model for House Price Prediction
  • Implemented Artificial Neural Network in R and Python
  • Performed comprehensive data collection and cleaning
  • Executed matrix manipulation in kdb+
  • Documented model architecture and implementation details
Hedonic & Spatial Pricing Models
  • Implemented Hedonic Pricing Model in R
  • Developed Geo-Spatial Pricing Model with custom distance metrics
  • Created comparative analysis with original ANN model
  • Built R-kdb+ framework for model deployment

Research Intern

Microsoft Research India / R.C. Bose Centre for Cryptology

Bangalore, India

May 2019 - July 2020

Conducted advanced research in cryptology and quantum computing, developing novel algorithms for the Hidden-Subgroup Problem and implementing Zero-Knowledge Authentication protocols.

Research Contributions

Hidden-Subgroup Problem Research
  • Created O(n²) algorithm for solved cases
  • Developed novel approach to quantum computing problems
  • Implemented efficient solutions for specific subgroup structures
Zero-Knowledge Authentication
  • Built proof-of-concept prototype
  • Implemented cryptographic protocols
  • Developed alternative proof for Fermat's Theorem

Technical Expertise

Programming Languages

Python Q/kdb+ R JavaScript SQL C++ C

Machine Learning & AI

Deep Learning Neural Networks XGBoost CatBoost TensorFlow PyTorch Keras Optuna

Data & Analytics

Plotly Dash Pandas NumPy SciPy Survival Analysis Time Series Statistical Modeling

Infrastructure & Tools

AWS Docker Kubernetes GitHub Actions PyQt React Node.js

Financial Mathematics

Risk Management Derivatives Pricing Portfolio Optimization Monte Carlo Stochastic Calculus Fixed Income

Specialized Domains

Mortgage Analytics Real Estate Modeling Cryptology Quantum Computing Signal Processing Optimization