zaltarba / deeplearning2

Hi there 👋

Quantitative Researcher with in-depth knowledge in finance, machine learning, statistics and econometrics. Stock markets enthusiast !

Education 🏫

• Polytechnique Paris - Master 2 Data Science - 2022/2023

  • Courses : Deep Learning, Computer Vision, Missing data and causality, Tail Events Analysis

• ENSAE Paris - Engineering program - 2019/2023

  • Courses : Statistics, Time series, Econometrics, Monte Carlo methods, Stochastic calculus, Online Learning

• Janson de Sailly - Classe Préparatoire aux Grandes Écoles - 2017/2019

  • Physics and Engineering class, PCSI-PSI*

Work Experiences 🏦

• Quant Research at Queensfield AI

  • On going since January 2024
  • Successfully implemented a market-neutral alpha strategy based on pair trading
  • Initiated preliminary research and explorations for a future PhD thesis

• Quant Researcher at Ostrum Asset Management

  • 6 months internship in 2023
  • Conducted research on the Hurst exponents across a wide variety of underlyings for low-frequency trading.
  • Development and Deployment of a Stop-Loss/Take-Profit mechanism for the Fixed Income Desk strategies.

• Quant Researcher at CACIB

  • 6 months internship in 2022
  • Performance modeling and forecast through econometric on low frequency for long-term financial planning
  • Implementation of machine learning models such as OLS, LASSO, SARIMAX
  • Developed a systematic feature selection methodology to identify key predictors in multivariate environments

• Data Scientist at BNP Paribas

  • 6 months internship between 2021 and 2022
  • Developed a robustness index for smartphones using econometric models to quantitatively assess product durability
  • Managed large datasets with Python in a cloud environment, optimizing data handling and computational efficiency
  • Enhanced data through cleaning processes utilizing NLP and clustering techniques

• Data Scientist at AFL

  • 3 months internship in 2021
  • Identified and characterized financial trajectories of municipalities by employing advanced clustering techniques
  • Conducted a thorough investigation into how social factors influence the financial health of municipalities
  • Implemented statistical tools and metrics to facilitate ongoing reporting

• Mathematics tutor at Ipesup

  • On going since 2019
  • Mathematics lessons and tutoring for students in CPGE

Personal and academic project 🎓

• Bayesian Statistics Project - Github - 2023
  The aim of this project is to group a set of time series into clusters, and to estimate the model describing each cluster. Based on the folowing article. More about it in this post.

• Hidden Markov Models Project - 2022
  Study of the article "A closed-form filter for binary time series", published by Fanaso and al. in 2021. The aim of this project is to study and benchmark the performance of the "Optimal Particle Filter", which is presented in Section 4.2 of the article, with the well-known Bootstrap Filter algorithm. More about it in this post.

• Hackathon with Ostrum Asset Management - 2022
  First prize for the ESG topic, focusing on the influence of companies' carbon footprints on their default risk

• Applied statistics project with Agence France Locale - 2020/2021
  Production of descriptive statistics on French communes for the AFL barometer, identifying financial trajectories followed between 2014 and 2019 with clustering

• Movie recommendation algorithm with Python - 2020
  Prediction of MovieLens database users' tastes by methods including clustering (k-means), PCA, matrix factorization using numpy, pandas, scipy libraries

Skills & interests

-Languages : French (native) 🇫🇷, English (TOEIC: 910), German (level B1)
-Programming languages : Python, SQL, VBA, DAX, LaTeX
-Softwares : VS Code, Excel, Power BI, Microsoft Access, Powerpoint
-Soft skills : Teamwork, Critical thinking, Curiosity, Autonomy
-Sports : Tennis 🎾, Boxing 🥊
-Culture and Hobbies : Chess ♟️, Cinematography 🎬, Aquariophilia 🐟