Data scientist

"Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world"

Atul Butte

Professional background

Data Scientist consultant

International Trade Centre - United Nation & Institute of Biomedical Research and Sports Epidemiology (INSEP)
Annecy - 2019
For ITC:

Under the overall guidance of a TMI Senior Market Analysis and the direct supervision of a Market Analyst, I contribute to the update of trade in services statistics. More precisely, I:

Scrapping:

  • Collect and process quarterly trade in services statistics from national sources.
  • Automatize data processing steps of trade in services data.
  • Share processed data with UNCTAD and WTO
  • Development:

  • Improve the error management system
  • Develop a standalone version
  • Develop imputation methods to replace missing values and outliers for the computation of trade indices
  • Workshop

  • Develop training materials
  • Contribute to the development of distance learning courses (ECOWAS and CARIFORUM)
  • Perform data quality checks
  • Contribute to the regional training delivery on data quality
  • For IRMES:

  • Data collection
  • Complete and create databases
  • Develop automation tools and programs
  • Create dynamic and visual tools
  • Institute of Biomedical Research and Sports Epidemiology (IRMES)

    National Institute of Sport, Expertise and Performance (INSEP)
    Paris - 2019
    Training manager: Bertrand Daille - Jean-François Toussaint - Adrien Sedeaud

    Orange Labs & Laboratory Andrology Gerontechnology Inflammation Modeling (AGEIS)

    Grenoble - 2018
    Training manager : Timothée Aubourg - Nicolas Vuillerme

    A study was conducted with Orange Labs on 26 peoples and they collected data from their phone conversation with a psychologist.

  • With my colleague Sylla Camara we have hundreds of backup conversation between patient and psychologist and we had two objectives
  • To investigate the potential structure existing behind phone conversational parameters in a population of fragile older adults.
  • Explore the corresponding opportunities/challenge in the health research area.
  • We create a Shiny application to show structure and cluster in a phone call in our dataset and to make them exploitable for another dataset.
  • Tasks to be completed: Machine learning, analysis technique, Shiny app designer, team work, investigate, clustering
  • International Trade Centre - United Nation

    Geneva - 2018
    Training manager : Christophe Durand - Christian Delachenal

    The International Trade Centre (ITC) is a joint agency of the United Nations and the World Trade Organization for trade-related technical cooperation in developing countries. ITC’s Trade and Market Intelligence section supports trade policy makers, trade support institutions and enterprises in developing countries in identifying opportunities for product and export market diversification. An R-based version « TradeOI v0.2 » had been created to analyse the evolution of quantities and unit values of Malawi trade transactions and for all the ASEAN countries.

  • My missions will be to improve the application « TradeOI v0.2 »; to introduce more flexibility; to process different kinds of trade data inputs; to include large trade transaction level datasets; to provide users with more computation options and a simpler user interface.
  • Tasks to be completed: Develop new TradeOI data import feature; to develop new computation features; to improve TradeOI user interface; to perform other tasks as required
  • Becton & Dickinson

    Grenoble - 2018
    Training manager : Didier Morel

    BD is a global medical technology company that is advancing the world of health by improving medical discovery, diagnostics and the delivery of care. BD leads in patient and healthcare worker safety and the technologies that enable medical research and clinical laboratories.

  • With my colleague Sylla Camara, we create an application Shiny for the research and development department of BD Pharmaceutical System which predict the interfacial tension (IFT) between solution and silicon in a syringe. For that, we use different analysis technique (Lasso, Random Forest, Constrained regression, ...) to find which variables are the more significant.
  • Tasks to be completed: Machine learning, analysis technique, Shiny app designer, team work
  • Adecco Switzerland

    Geneva - 2017

    Summer time worker

  • Missions: handling, building worker, chain work
  • Loc'one Rossignol

    Grenoble - 2014/2016

    Ski technician

  • Missions: Seller, renter, repairer, cash collection