Freelancer profile translated to English.

Description

I am a Data Scientist with a strong mathematical background.

My studies and practical experience in data science allow me to respond to a wide variety of problems:

From establishing a classification procedure for Stochastic Differential Equations - - and pricing models, to web scraping and using the Google Analytics, Facebook and Instagram APIs to collect and analyze customer data.

The passion and motivation in terms of Data Science is supported by the large number of personal projects in which I have been involved over the years - prediction model for tennis, emergence of language between reinforcement learning agents, participation in Data For Good France etc...

In recent years, I have gained experience in the following technologies:

- Python (pandas, sciit-learn, matplotlib) with Jupyter and PyCharm

- Spark

- Tensorflow

- R

- Hive

- Amazon AWS and its boto API

- The Google Analytics, Facebook and Instagram APIs.

Whatever your request, I can adapt to meet it!

Languages

English
Fluent
French
Native or bilingual

Workplace preferences

Can work on-site

Paris (up to 50km)

LAJAVANESS
Research and Development Data Scientist
DIGITAL AND IT
March 2017 - September 2017 (6 months)
Paris, France
As an R&D Data Scientist, I was in charge of developing machine learning models that meet the problems of our customers:

- NLP and development of an API allowing the automatic processing of emails sent to customer service.
- NLP and Deep Learning to extract postal addresses from emails.
- Maintenance of a model developed in Spark and designed to identify the optimal times to call customers.
- Development of a real-time discount pricing model during negotiations.

Technologies:
- Python (pandas, scikit-learn, nltk)
- Tensorflow
- Spark
- Javascript
Python Scikit-learn nltk Machine learning Spark SQL TensorFlow R
Cheerz
Data Scientist
E-COMMERCE
June 2016 - November 2016 (6 months)
Data Scientist Intern
During my experience at Cheerz, I did many things:
Identifying ways to increase conversion on the website and application. To do this, I worked with multiple data sources:
- The company's internal data.
- The Google Analytics (tracking data) and Facebook APIs.

I also built an algorithm collecting potential influencer accounts on Instagram and potentially interested in Cheerz products, using the Instagram API.

In addition, I carried out analyzes on customer data and made dashboards to support the decisions of the marketing department.

Finally, I implemented an A/B Testing tool using a Bayesian approach instead of the usual frequentist approach. The purpose of this tool was to counter peeking and early-stopping behaviors.
Python Google Analytics Facebook Instagram Marketing AB testing SQL R PostgreSQL
Laboratoire d'Analyse et de Mathématiques appliquées
Machine Learning Research
RESEARCH
May 2015 - October 2015 (6 months)
Paris, France
As a research intern, I developed a two-step method for classifying Stochastic Differential Equations, using the Bayes classifier:
- First, estimate the parameters of the stochastic differential equation using a maximum of likelihood.
- Then, using the estimated values, construct an approximation of the bayes function and make a decision based on it.
R Machine learning Writing

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Master Probability and Statistics
Université Paris-Est Marne la Vallée
2015
Durant mon master orienté recherche, j'ai étudié les probabilités et les statistiques théoriques et appliquées: - Chaînes de Markov - Processus stochastiques - Probabilités - Statistiques - Statistiques computationnelles: méthodes de monte-carlo, monte-carlo markov-chain methods - Séries temporelles - Calcul stochastique
Master Specialized in Data Science
ENSAE ParisTech
2016
Ce master m'a donné une approche plus pratique de la Data Science: - Machine Learning et Data-Mining -Database et web -Statistiques Computationelles (Monte Carlo Markov Chain) - Econométrie du Marketing - Analyse Statistique des données de graphs - Outils pour l'analyse de de données massives - Spark, Pig. -Hadoop -Bootstrapping et resampling - Statistiques Bayésiennes De plus, j'ai eu de nombreux projets impliquants la pratique de python, python pour la data science - scikit-learn, pandas, matplotlib - Spark, Pig, SQL et R.

Data Scientist

Gabriel Ducrocq

data scientist

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