DEVIPRASAD NIRJOGI

Hyderabad

Summary

Dynamic research consultant with a proven track record at World Quant, specializing in machine learning and statistical modeling. Developed high-Sharpe ratio models and innovative alpha strategies. Adept at leveraging Python and SQL to drive impactful insights, while demonstrating strong analytical skills and a commitment to excellence in quantitative finance.

Overview

year of professional experience

Work History

Roopastra

Founding Member

Hyderabad

12.2024 - Current

AI-powered virtual try-on prototype, architecting the core solution with Python and Computer Vision to successfully demonstrate real-time garment overlay capabilities.
Engineered the initial machine learning models for body detection and created a proof-of-concept for an AI-driven, occasion-based style recommendation engine tailored for the Indian retail market.
Established the company's competitive advantage by leading in-depth market analysis and defining a unique selling proposition based on real-time processing, cultural relevance, and a hardware-light model

Research Consultant - Remote Internship

World Quant

01.2025 - Current

Created novel alpha strategies across multiple asset classes and timeframes
Developed and backtested quantitative models for global equities markets
Implemented machine learning techniques to enhance alpha signal generation
Conducted research on market anomalies and factor-based investment strategies
Contributed to the firm's alpha library with several high-Sharpe ratio models
Worked with 200+ financial vector datasets

Education

Bachelor of Science -

Indian Institute of Technology

Madras

05-2026

Skills

Python
SQL
Statistical modeling
PineScript

Interactive Brokers
Machine learning
Time Series Forecasting
Deep Learning

Projects

Option Pricing Toolbox | Python, NumPy, Matplotlib, Jupyter

Implemented Black-Scholes-Merton model with comprehensive Greeks computation for accurate derivative pricing.
Built implied volatility surface estimator utilizing real market data for enhanced pricing accuracy. Simulated path-dependent options using Monte Carlo methods for complex derivative valuation.

Portfolio Optimization | Python, CVXPY, Pandas

Developed mean-variance portfolio optimizer with advanced risk constraints and comprehensive backtesting for Stocks and Cryptocurrency
Integrated Sharpe ratio and Sortino ratio maximization with robust backtesting module for performance analysis. Added EigenVector optimization model extension for maximum returns of the portfolios.

Accomplishments

Quantageddon 2025 - First place
WorldQuant PowerPool Alphas 2025- Winner
50kafunda - Winner

Timeline