I'm in my final year of BCA (part of a 5-year MCA at IIPS, DAVV).
Recently, ranked 1st at iRage AlgoArena and top 1.5% at Wunder Predictorium by Wunder HFT globally this year.
Completed a three-month internship at Walford Capital as a Quant Intern and currently working as a
Research Consultant at WorldQuant. Lately picking up Rust.
Now aiming to build professional-grade low-latency models using C++ and deepen understanding of
statistics, stochastic processes, and market microstructure.
Snapshot
Winner at iRage AlgoArena 2026, awarded a cash prize of Rs 200k)
Interned at Walford Capital (Quant)
Research Consultant at WorldQuant
Professional Experience
Hands-on exposure to pricing models, alpha generation, and research workflows.
Research Consultant — WorldQuant
Apr 2025 – Present · Remote
Conduct alpha research and feature engineering across multiple datasets.
Optimize datasets and pipelines using ML and APIs for automation.
Alpha Research
Quant Intern — Walford Capital
Mar 2025 – Jun 2025
Being a part of the quant team at the startup, having its origin from IIT Madras, Built pricing models using Kalman-based state-space models, Monte Carlo methods, and Black–Scholes.
Developed a meta-model pipeline integrating: causal inference, XGBoost, PCA, K-Means, PPO RL for portfolio optimization, volatility-based backtester, and sentiment analysis (crewAI).
Assisted in model testing, strategy optimization, and dataset management.
Offered a role (cash or equity), but chose to focus on deepening statistical and analytical foundations.
Ranked 74th / 4,900+ global participants (Top 1.5%).
Engineered predictive ML models to forecast multiple variables across varying horizons using anonymized LOB data.
Quant-Insider-Market-Making-Challenge
IIT(ISM) Dhanbad · Dec 2025
Executed Live Market Making Strategy based on Avellaneda–Stoikov equations
Used 5-level orderbook depth data for finding mispricing at microstructure level and placing trades accordingly
Used Nubra Python SDK for fetching realtime websocket data (especially 20-level orderbook depth data), made a simulator similiarly like the exchange matching system
Executed the strategy on all the options of Nifty (in total 440 options) in realtime simultaneously on real world data
Building a full-stack decoupled ZeroMQ microservices architecture for Nifty 50 basis arbitrage, currently improving the achieved 0.25ms median paper-execution latency.
Integrating live statistical inference and ONNX-compiled ML risk engines for real-time signal verification.