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Machine Learning & Data Engineer

6 years of experience
3 industries
7 certficates
Employed Open to opportunities
As a Machine Learning & Data Engineer with 6 years of experience, my work consists of designing and implementing data-driven solutions to help companies leverage the power of their data.

Practical information:

  • Age : 28 years
  • Nationality : French
  • Residency Status : B Permit holder
  • Years of experience : 6 years
  • Highest academic degree : Master Of Science in Information Technology
  • Within UBS' Group Compliance, Regulatory and Governance, my role is to build data-driven solutions for non-financial risk management.
  • Mission 1 : implement Databricks workflows to monitor KPIs for Compliance Risk AI models. Tools: python (pandas, pyspark), Gitlab, Databricks
  • Mission 2 : refactor AI model's data sourcing using an event-based paradigm. Tools : python (Airflow) Kafka
  • Within the Datalab team of the Strategy & Development department, I worked on several Data & ML projects. Work ranges from proof of concept (PoC) to building data pipelines and deployment in production. Following are a few examples of missions I worked on.
  • Mission 1: Recommender System. Implementation of a recommender system that suggests companies with high default risk. The algorithm is based on criteria of similarity with the companies already reviewed by the risk underwriters. Tools: Python (pandas, sklearn, pySoT), Gitlab, optimization, Surrogate Modeling, Gower Distance
  • Mission 2: Complementary insurance. Automating of product profitability reporting, analyzing customer behavior and segmentation, predictive analysis of price sensitivity. Tools: Python (pandas, sklearn, airflow), Gitlab, Airflow, Docker
  • Mission 3: Balance sheet forecast following the Covid crisis. Modeling the impact of the Covid crisis on company balance sheets in order to adapt the Risk underwriting strategy. Tools: Python (pandas, pymongo), Gitlab
  • Mission 4: REST API for an external client.
    Design and implementation of REST API for a customer in the banking sector that allows them to search and identify a company, buy default risk scores and payment behavior insights. Tools: Python (Flask, pandas, cx-oracle, sqlalchemy), Docker, Kubernetes, AWS (API Gateway), Elastic Search, Oracle, Gitlab
  • Within the Machine Learning & Data Lab team, I contributed to the development of the AI offer. I carried out internal R&D work and participated in missions with external clients.
  • Mission 1 : review of the state of the art of Deep Reinforcement Learning with an application to autonomous driving. Tools: python, tensorflow, keras, pytorch, opencv, torcs, airsim.
  • Mission 2: implementation of a scanned contract processing tool for a player in the pharmaceutical industry. Tools: python, jupyter notebook, tesseract ocr, nltk, tensorflow, glove.
  • Within the "Centre de Compétences d'Informatique Cognitive" (Skill Center of Cognitive Computer Science), I carried out POCs (prrofs of concepts) on the use of NLP to analyze customer verbatims. Tools: Python, NLTK, Gensim, Keras, Tensorflow, Pandas.
  • I contributed to the development of conversational agents intended for Orange employees. Tools: IBM Watson, dialogflow (Ex from Google, python, keras, tensorflow, pygal.
  • Within the Signal & Communications department, I carried out research work on Spectral Clustering algorithms which resulted in the publication of a conference paper.
  • My mission was to implement a similarity function (kernel) for these algorithms and evaluate its performance. Tool: matlab.
  • Link for the paper :

Master Of Engineering

IMT Atlantique (Ex Télécom Bretagne) - Brest - France

August 2015 to August 2019
Generalist degree in Information technology with a specialization in Machine Learning and computer vision.

Erasmus Exchange Semester

Reykjavik University - Reykjavik - Iceland

January 2017 to May 2017
Academic semester at the School of Computer Science in Reykjavik University.

Bachelor's Degree in Mathematics

Université de Bretagne Occidentale - Brest - France

September 2015 to June 2016
I took the courses of this bachelor in parallel with my main Master of Engineering at IMT Atlantique (Ex. Telecom Bretagne - Intitut Mines-Télécom) in order to deepen my knowledge and understanding of the following disciplines: topology, general algebra, differential calculus, statistics and stochastic modelling.

Preparatory Classes For "Grandes Ecoles"

Classes Préparatoires Aux Grandes Ecoles - MPSI/MP

September 2013 to August 2015
Intensive preparatory courses for the french "Grandes écoles" contests.

Apr 4, 2018
  • Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications
  • Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow
  • Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data
  • Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering

Sequence Models

April 4,2018
Build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering.

Convolutional Neural Networks

November 24, 2017
Build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.

Structuring Machine Learning Projects

Aug 22, 2017
Diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning.

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Aug 21, 2017
Learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow.

Neural Networks and Deep Learning

Aug 17, 2017
Build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to different applications.

Machine Learning

Oct 29, 2016
Build and train main Machine Learning models (regression, classification, support vector machines, neural networks, Kmeans, recommender systems (collaborative filtering)
  • Languages : python, SQL, Java
  • Frameworks : Databricks, Matlab
  • Data Extraction : cx-oracle, sqlalchemy, pymongo, requests
  • Data Processing & Analytics : pandas, pyspark
  • Orchestration : Airflow, Databricks
  • Model Serving & APIs: Flask and FastAPI
  • Machine Learning Modelling : scikit-learn, LightGBM, tensorflow, keras, pytorch
  • Oracle
  • MongoDB
  • Elasticsearch
  • English
  • French
  • German
  • Hearthstone, 7 wonders, splendor
  • President of chess club at school