With a skill set focused on mathematical modeling (optimization, statistics, statistical learning, Bayesian modeling), I have also developed a great interest in programming (particularly functional programming). You can find my resume below.

Skills

• Programming: Python, R, Scala, Haskell, bash, LaTex, Java, Matlab, SQL
• Statistical Learning: Deep Learning (Pytorch, Tensorflow), Computer Vision (OpenCV), Bayesian (stan, pystan), causality (Causality by J.Pearl), Boosting, SVM… (Elements of Statistical Learning)
• Data Engineering: Snowflake, Spark, Kubernetes, Docker, S3, Elastic Search.
• Languages: English, native in French, basic Spanish and Italian.

Experience

• Fonction Labs, Paris (Dec 2023 - Now) - Co-Founder
  keywords: Data Science, Data Engineering, Software Design Fonction Labs builds and deploys data pipelines, transforming any business for the next AI revolution.

• BCG Gamma, Paris (Nov 2021 - Dec 2023) - Data Scientist
  keywords: Data Science, Data Engineering, Software Design

  • Designed and developped an automated anomaly detection software for images based on state-of-the-art techniques (computer vision) from scratch to production.
  • Designed new NLP similarity metrics for a co2 emissions search engine
  • Data Pipeline design as a Data Engineer, using Functional Programming principles (Snowflake, Spark)

• Amadeus, Boston (Jun 2019 - Dec 2020) - Machine Learning Engineer
  keywords: Data Science, Statistics, Bayesian Inference

  • Developed an Automated Root Cause Analysis tool for production using statistics and machine learning (architect solution). Exploration of the causality litterature (Causal Graph, Judea Pearl)
  • Created a popularity prediction model using Generalized Mixed Effect Models for Cache Managing.
  • Developped new ranking comparisation metrics for recommendation systems to assess its optimality.

• Elum Energy, Paris (Mar 2018 - Aug 2018) - Optimization Engineer (Research Internship)
  keywords: Microgrids optimization, Energy Planning, Operation Research, Reinforcement Learning

  • Developed an new Energy Management Systems for photovoltaïc microgrids for major customers in the energy sector.

• Parrot, Paris (June 2017 - August 2017) Research Scientist (Research Internship) keywords: Deep Learning for object detection, pytorch, Triplet Network, Semi-Supervised Learning

  • Implemented an image comparator able to recognize objects and persons of the same class using triplet loss networks and outperformed the previous system. [Pytorch].

Education

• Columbia University, New York 2018 - 2019 | Master of Operation Research
  Studies : Machine Learning, Stochastic Process, Optimization, Game Theory, Graph Theory

• École Polytechnique - Institut Polytechnique, Palaiseau (2015 - 2018) | Master of Energy and Applied Mathematics. Diplôme d’ingénieur de l’Ecole Polytechnique.
  France’s Leading Engineering and Science University. Studies : Statistical Learning (SL), Statistics, Deep Learning, Smart Grids

• Sainte-Geneviève, Versailles (2013 - 2015)
  Classe Préparatoire aux Grandes Ecoles. Studies : Mathematics, Physics, Chemistry

Research Projects

• Backtesting Python Environement (2021) - Personal Project
  Developped a Backtesting environnement, exploring trading strategies for crypto and stocks.

• Generalized Non Linear Mixed Effect Models Library (2020-2021) - Personal Project
  Developped a non linear GMEM library for fast training and big data. (pytorch)

• Time Series Generation (Fall 2019) - Academic Project at Columbia University
  WaveNet architecture for Continuous Time Series Generation in finance simulation. (pytorch)

• Neural Network Compression using Determinantal Point Processes (Spring 2019) - Academic Project at Columbia University
  Determinantal point process sampling for neural network compression. (tensorflow)

• Network Optimization (Spring 2019) - Academic Project at Columbia University
  Near Optimal Strategies for Targeting in Networks using submodular optimization (Networkx)

• Submodular Optimization for Summarization (Spring 2019) - Academic Project at Columbia University
  Submodular Optimization for Multidocument Summarization.

• Statistical Learning for Energy Consumption (2018) - Academic Project at Ecole Polytechnique
  Exploration of Statistical Learning Algorithms applied to energy consumption.