Jan 2025 – Jun 2025 · 5 mos · Remote

• Collaborated with the engineering team to streamline data pipelines and improve execution algorithms.
• Stochastic Interest Rate Model Development: Developed and calibrated Vasicek and CIR models using US Treasury data for accurate bond pricing and interest rate forecasting.
• Hybrid Heston-LSTM Model for Volatility Forecasting and Option Pricing: Combined Heston model with LSTM neural networks to enhance volatility forecasting and improve option pricing accuracy.
• Developed a standalone IFRS 9-compliant Expected Credit Loss (ECL) model, including 3-stage classification logic, macroeconomic scenario integration, and lifetime loss estimation based on synthetic loan portfolio data.
• Contributed to articles on quantitative finance topics, including machine learning applications and algorithmic trading strategies.

May 2024 – Sep 2024 · 4 mos · Glasgow, Scotland, United Kingdom · On-site

• Enhancing Option Pricing Models through Jump Diffusion Processes in Finance
• Conducted an in-depth comparison of the Black-Scholes, Merton Jump-Diffusion, and Kou models for option
• pricing, emphasizing their ability to capture market anomalies and jumps in asset prices.
• Implemented these models in Python, using real market data for calibration to ensure robust and accurate
• pricing.
• Utilized Yahoo Finance for obtaining US 3-month Treasury data as a risk-free rate benchmark and integrated
• this data into the models to enhance pricing accuracy and risk assessment.
• Employed various accuracy metrics such as RMSE (Root Mean Square Error) and MAE (Mean Absolute Error)
• to quantitatively compare the performance of the models.
• Conducted sensitivity testing to validate the robustness of the models across different market scenarios and
• conditions.

Sep 2023 – Sep 2024 · 1 yr · Glasgow, Scotland, United Kingdom · On-site

Jun 2020 – Apr 2023 · 2 yrs 10 mos · India · Hybrid

• Experienced in financial markets: Developed and deepened expertise in market and technical analysis.
• Classified high-probability setups by analysing data based on hierarchy of support, duration of support, call and put
• open interest analysis, futures open interest analysis, moving averages of 200 and 500 (weekly and daily time
• frame), volume analysis, multiple time frame analysis, and weekly closing patterns.
• Achieved a remarkable return on capital of 20000 Pounds; approx. 90 % Return on Capital in one year, winning the
• Zerodha Trading Challenge.
• Incorporated machine learning techniques to enhance the accuracy of signal predictions and optimize trading
• strategies.

Oct 2018 – Jan 2020 · 1 yr 3 mos · Chandigarh, India · On-site

• Applied the Navier-Stokes equations for comprehensive mechanical model analysis, leading a team to enhance the accuracy of fluid dynamics simulations.
• Conducted detailed mathematical analysis and optimization of structural models, specifically targeting centrifugal compressors in water pumps.
• Improved the efficiency and performance of centrifugal compressors through advanced computational techniques, using machine learning to predict input parameters for model refinements using Python.

May 2016 – Jul 2016 · 2 mos · Lucknow, Uttar Pradesh, India · On-site

• • Engaged in structural analysis, aerodynamic simulations, and maintenance tasks, applying mathematical techniques and machine learning algorithms in Python to optimize aircraft performance