Debajyoti Nandy

Product Engineer

Chicago, Illinois, United States11 yrs 11 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in High-Frequency Trading algorithms.
Strong background in Applied Mathematics and Statistics.
Experience in quantitative research and model validation.

Stackforce AI infers this person is a Quantitative Researcher specializing in Fintech and High-Frequency Trading.

Contact

Skills

Core Skills

High-frequency TradingEquity Option TradingModel Validation

Other Skills

AlgorithmsC++Data AnalysisDerivativesEvent ManagementFixed IncomeInterest Rate DerivativesLaTeXMachine LearningMarket ResearchMathematical ModelingMatlabMonte Carlo SimulationOptimizationOptions Theory

About

Quantitative researcher with expertise in Applied Mathematics, Statistics, Machine Learning and High Frequency Trading algorithms. Like to study theoretical mathematics and Physics in my spare time.

Experience

11 yrs 11 mos

Total Experience

3 yrs 11 mos

Average Tenure

6 yrs 3 mos

Current Experience

Imc trading

2 roles

Quantitative Researcher

Aug 2025 – Present · 10 mos

Gardening Leave

Quantitative Researcher

Feb 2020 – Jul 2025 · 5 yrs 5 mos

Research in high frequency US Equity Option Trading, India Index Options trading.

Equity Option TradingHigh-Frequency TradingDerivativesOptions Theory

Panagora asset management

Summer Intern, Equity Strategies

May 2019 – Aug 2019 · 3 mos · Greater Boston

Summer Intern at Stock Selector Equity Strategies team

Goldman sachs

2 roles

Associate

Jan 2018 – Jun 2018 · 5 mos

Quantitative Strategist
Led internal model validation of Asset Liability Management models along with Model Risk Management (MRM) team
Added back-testing and regression-testing framework to test the model assumptions and calibrated parameters

DerivativesModel Validation

Analyst

Jul 2014 – Dec 2017 · 3 yrs 5 mos

Quantitative Strategist
Built statistical models for Asset Liability Management of Fixed Income (Rates, Credit, Emerging Markets) and Equity (One-Delta) desks
Analyzed raw market volume data of Interest Rate and Credit Flow Derivative contracts and implemented time series analysis and regression based liquidity forecasting models
Researched academic developments around non-linear optimization to better model the non-linearity introduced by Value-at-Risk (VaR) constrains in the optimization problem
Developed and implemented VaR and liquidity constrained funding carry optimization model along principles of Sequential Linear Programming and Box Constrained optimization
Programmed an automated rebalancer in S-lang which can perform trades resulting from the optimization and project asset and corresponding liability requirement used for benchmark debt issuance and Balance Sheet resource planning