Quantitative Research Internship Summer

Steven Worick

linkedin.com/in/steven-worick/ +**(0)6 59 40 24 96, github.com/Stevenworick

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Education

ENSAE Paris - Institut Polytechnique De Paris Massy-Palaiseau, FRANCE Specialized Master Finance and Risk Management 2021-2022 Relevant Courses Algorithmic Trading, Machine Learning, Deep Learning, Data science, Time Series, Computer Science Universit e Paris Dauphine - PSL Paris, FRANCE

Master’s Degree in Economic and Financial Engineering (272), Quantitative Finance 2020-2021 Relevant Courses Probabilities, Linear Algebra, Statistics, Data Science ESILV - L eonard de Vinci Graduate School of Engineering Paris, FRANCE Master’s Degree of Engineering – MEng, Financial Engineering 2018-2021 Relevant Courses Quantitative Finance, Computer Science, Risk Management Work Experience

Soci et e G en erale Corporate & Investment Banking New York, USA Quantitative Researcher, Algorithmic Trading - Electronic Market Making in FIC Team May. 2023 - Present

• Develop a volatility regime detection model, to identify critical transition points between different states, significantly improving hedging strategies by 5%.

• Designed and developed an end-to-end process to predict intraday volatility of G10 currencies using a linear model, enhanced with alpha signals derived from cross-asset classes including commodities, bonds, equities, derivatives, and sentiment data

• Researched and developed execution algorithms capable of recognizing toxic clients using Market Microstructure analysis, including TWAP client identification

• Developed post-trade analysis metrics to identify influential variables in the trading decision model using a customized confusion matrix, and calculated end-to-end D2C and D2D latencies to quantify our latency impact Natixis Corporate & Investment Banking Paris, FRANCE Algorithmic/ Quantitative Trading Intern Jun. 2022 - Dec. 2022

• Worked on tick-by-tick Bond Futures price prediction for Government Bonds Desk (SSA) using advanced machine/deep learning methods such as stacking model including LSTM, CatBoost and Linear Regression

• Researched and implemented advanced time series denoising techniques for mid-price prediction, utilizing both Wavelet denoising and Fourier Transform filtering, resulting in a 8% improvement in prediction accuracy.

Soci et e G en erale Corporate & Investment Banking Paris, FRANCE Cross Asset Financing Desk Trader Assistant in apprenticeship Sep. 2020 - Sep. 2021

• Implemented and improved decision tools (collateral optimization, positions term structure, historized prices received from tri-party agents).

Project

Ostrum Asset Management Paris, FRANCE

Hackathon Apr. 2022 - Apr. 2022

• First prize winner of the competition, the subject was “Implied volatility (CBOE VIX) Link Project GitHub directional methods Forecasting using Machine Learnings”.

• Replicated the results and proposed areas for improvement thanks to the calculation of the Hurst exponent and the cross validation on time series. The VIX is designed to produce a measure of constant, 30-day expected volatility, derived from real-time, mid-quote prices of S&P 500 index call and put options. E cole Normale Sup erieure - Data Challenge Paris, FRANCE Real estate price prediction by Institut Louis Bachelier Jan. 2022 - Apr. 2022

• Regression with XGBoost, Random Forest. Link Project GitHub

• Added new features based on images prediction with Convolutional Neural Network and VGG16.

• Multi-Processing running time optimization for calculation of distances. Skills

Languages: English (fluent), French (native), German (working knowledge) Certifications: DeepLearning.Ai, AMF, BMC

Programming: Python, SQL, C#, C++, VBA, R

Technologies: Pandas, Numpy, Matplotlib, Scikit-Learn, Tensorflow, XGBoost, StatsModels, Azure, Git, CMD, PyTest. Industry Knowledge: Market Microstructure, Neural Networks, Market Making, Machine Learning, Electronic Trading, Trading Strategies, Low-Latency, Limit Order Book.

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