Thomas L.

Data Scientist

660 dollar
6 years

My experience


YsanceJanuary 2020 - Present

Creating and conducting Deep Learning training programs for colleagues and clients (130+ students & 16H+ of lectures so far)

• Focused on both theoretical explanations and practical implementations of State-of-the-Art NLP models for English and French languages

Carried out several Data Science projects for diverse clients

• Segmented visitors and tailored recommendations of exhibitors for one of the biggest furniture trade show in Europe (Maison & Object by SAFI)

• Implemented a novel customer segmentation methodology emphasizing on efficiency of means of communication for SoloInvest

• Undertook initial analyses of the transaction databases of Century21 in cooperation with other colleagues

Use of : PyTorch, scikit-learn, SQL, GCP, AWS

FreelanceOctober 2017 - January 2020

Designed and developed the AI engine for a startup specialized in customer journey analysis
Dec. 2019 - Jan. 2020

• Adapted current state-of-the-art attention model for French language CamemBERT (nov. 2019) based on RoBERTa's architecture (jul. 2019, Facebook AI Research) in order to perform text classifications with very few training examples

Developed several automated cryptocurrencies-trading bots for a group of investors
Oct. 2017 - Oct. 2019

• Devised a prototype of a state-of-the-art algorithm for automated event-driven trading using CNNs applied to neural tensor networks’ event embeddings

• Conceived, implemented and automated several innovative strategies to exploit the markets’ inefficiencies and trends

• Arbitrated between 10+ marketplaces with very heterogeneous technicalities

Use of : Pytorch, Tensorflow, Keras, transformers, Scrapy, Beautiful-Soup, Request, Flask

Collective ThinkingMay 2016 - September 2017

Worked on a software that analyzes data from any written medical source, to sum up patients’ cases and suggest medical diagnoses and potential alerts for health institutes

• Used complex models including RNNs of GRUs/bi-LSTMs, own-trained word embeddings, transfer learning, multi-task learning, and zero-shot learning

• Revamped and optimized our prototype’s data pipeline from the processing of the raw scans and models’ training to the actual predictions made inside our app

• Read numerous research papers pertaining to diverse aspects of our models and domain of application to exploit the latest advances and most relevant techniques

• Handled confidential data in a highly regulated field with no margin for error

• Highly sparse, noisy and biased data and labels: 68K+ labels (ICD-10) with sometimes no incidence occurring for the last 10 years

Use of : Tensorflow, Keras, Gensim, NLTK, Docker, GPGPU computing, Linux, SQL, Requests, Flasks

OrangeApril 2015 - September 2015

• Built a generalist AI for Atari games combining reinforcement learning and deep learning using a technique pioneered by Goggle DeepMind in December 2013 called Deep Q-Learning

=> use of python's library Theano and RL-Glue to create the agent and interact with the Arcade Learning Environment
=> use of Lasagne on top of Theano and cuDNN for accelerated results

A web site containing information about the procedures and results is available at

• Solved a Kaggle competition with the use of Deep Learning
=> use of Keras and Lasagna libraries on top of Theano

• Conducted a series of interviews with experts evolving in the Data Science industry and creation of a blog containing the published interviews

• Analysis of the Data-Science-as-a-Service ecosystem
• Analysis of the evolution of the Deep Learning architectures
• Analysis of the past evolution and prediction for the GPU market

WisemetricsAugust 2014 - January 2015

• Developed sentiment analysis classifiers combining NLP methods with information extraction and machine learning algorithms in Python and R.

=> acquisition of insights regarding the emotional status, basic psychology and human values of the audience of one's Twitter account
see "Explore your Twitter Audience Psychology" on

• Developed a web tool to analyze users’ Twitter audience in order to rank them amongst other users and give insights about their followers.
=> development and smoothing of a ranking strategy and optimization of sampling and ranking methods to get an quick online grading of one's Twitter account

• Developed internal tool to hunt for interesting hash-tags on Twitter and forecast the evolution of their future usage and impact on Twitter.
=> development of an hash-tags scoring strategy, sampling and forecasting of the score time-series

• Designed and implemented innovative add-ins to the existing products.

Orange France, InternshipJune 2012 - August 2012

• Studied the penetration and market share of very high speed broadband regionally
• Implemented statistical tools to report on evolution of key network performance indicators
• Analyzed big databases (10M+ lines)

My stack


SQL, R Language, Python, LaTeX


Spanish, Twitter, Natural Language Processing (NLP), Artificial Intelligence, Data analysis

Big Data


Business Intelligence


Analysis methods and tools

Agile Methodology

IT Infrastructure

Linux, Docker, Google Cloud Platform (GCP)


Flask, BeautifulSoup, Pandas, AWS, Scrapy, Machine Learning

Embedded and Telecom


Machine Learning

TensorFlow, Deep learning, PyTorch, Scikit-Learn, Reinforcement learning, Keras

My education and trainings

Master of Science / MS, Data science, machine learning, data analysis, optimization theory, game theory, sociology - Ecole nationale de la Statistique et de l'Administration économique2011 - 2016

Classes Préparatoires aux Grandes Ecoles – Mathematics and Physics Major - Lycée Janson de Sailly2009 - 2011