Bertrand Beaufils

data scientist 4+ years experience

Moves to Paris, Paris, La Rochelle, Bordeaux

  • 48.8546
  • 2.3477
  • Indicative rate €555 / day
  • Experience 2-7 years
  • Response rate 100%
  • Response time 2h
Propose a project The project will only begin when you accept Bertrand's quote.

Availability not confirmed

Part time, 2 days a week

Propose a project The project will only begin when you accept Bertrand's quote.

Location and geographical scope

Location
Paris, France
Can work in your office at
  • Paris and 50km around
  • Paris and 20km around
  • La Rochelle and 50km around
  • Bordeaux and 20km around

Preferences

Project length
  • ≤ 1 week
  • ≤ 1 month
  • Between 1-3 months
  • Between 3-6 months
  • ≥ 6 months
Company size
  • 1 person
  • 2 - 10 people
  • 11 - 49 people
  • 50 - 249 people
  • 250 - 999 people
+2 autres

Verifications

Languages

  • Français

    Native or bilingual

  • Anglais

    Full professional proficiency

  • Allemand

    Limited working proficiency

  • Espagnol

    Basic

Skills (20)

Bertrand in a few words

J'ai travaillé de manière très autonome sur différents projets "from scratch" : de la collecte/nettoyage et analyse de données à la conception de modèles adaptés jusqu'à l'industrialisation.
Travailler en free lance me permet de renouveler les challenges et découvrir de nouveaux secteurs et type de données. Je suis d'ailleurs actif sur Kaggle pour être au niveau de l'état de l'art sur les différentes problématiques en Data Science.
Je recherche toutes durées de mission, privilégiant des déplacements ponctuels plutôt que journalier.

Experience

OpenClassrooms - Openclassrooms

Education & E-learning

Mentor parcours Data Scientist / Data Analyst / Ingénieur Machine Learning

Paris, France

October 2019 - Today

Sysnav

Defense & Military

PhD in Data Science

Vernon, France

January 2017 - Today

Development of an activity recognition algorithm for ankle-mounted and wrist-mounted inertial device recorder with an innovative machine learning approach
• I collected and organized the data set
• I computed relevant features for the task based on topological data analysis, dynamic time warping and functional data analysis
• I computed an efficient prediction function combining gradient boosting and deep learning (CNN, LSTM, dense network) algorithms
• I optimized the code and developped an executable program
Currently used in clinical studies (supported by the OpenHealth Institute):
• Parkinson: tremor and dyskinesia crises detection
• Duchenne myopathy: stairs and running detection
Supervision of a team of 6:
• I attended a formation of management from SLP management
• I was in charge of recruitment: I designed technical tests and I led the interviews

Sysnav

Defense & Military

Data Scientist

Vernon, France

April 2016 - January 2017

Development of a stride detector algorithm for an ankle-mounted inertial device recorder with an innovative machine learning approach
• I collected and organized the data set
• I computed relevant features for the task based on patterns detection and functional data analysis
• I computed an efficient prediction function combining gradient boosting and deep learning (CNN, LSTM, dense network) algorithms
• I optimized the code and developped an executable program
• I monitored the code maintenance for clients

Safety line

Aviation & Aerospace

Data Science Algorithm Developer

Paris, France

September 2014 - July 2015

Development of a supervised learning algorithm applied to the flight data of black boxes I worked with a PhD student in Data Science co-developing the algorithms used for his thesis based on:
• Functional data analysis: wavelet, principal components analysis
• Features selection algorithm: permutation feature importance
• Random Forest

Probability, Statistics and Modeling Laboratory

Education & E-learning

Data Science Algorithm Developer – Internship

Paris, France

May 2014 - August 2014

Development of a Bayesian PAC algorithm for clustering with Gaussian mixtures
I developed the algorithm based on the work of B. Michel and S. Gaiffas ”Sparse Bayesian Unsupervised Learning”:
• Variable selection and estimation of the number of clusters based on Gaussian mixture models in high dimension
• Metropolis-Hastings algorithm with clustering-oriented greedy proposal

Education

Certifications

charter modal image

Success is a team effort

Contribute to this success and the community's professionalism by signing the Freelancer Code of conduct

Sign the code