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Xuanzhou WangXW

Xuanzhou Wang

Data Scientist, Expert Python, Ph.D.

€1,000/day
Paris, FR
8-15 years

Average response time: 1 hour

Freelancer profile translated to English.
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About Xuanzhou

Holder of a Ph.D. in Optimization and Applied Statistics, XuanZhou has strong skills in programming, data management, statistical analysis, and data science, enabling her to navigate complex subjects with agility.

XuanZhou is proficient in Python and SQL software and languages and has several experiences in the industry sector.
  • Chinese

    Native or bilingual

  • French

    Native or bilingual

  • English

    Fluent

Can work on-site
Paris (up to 50km)

Experience

  • ENGIE - Entreprises & Collectivités
    Data Scientist
    ENERGY AND UTILITIES
    February 2020 - Today (6 years and 3 months)
    Paris, France
    - Acquisition, audit, and interpretation of ERL (Radio Linky Emitter) data
    - Studies on load curves
    - Proposal of methods and implementation of ML algorithms for the "Ma Conso +" service: monthly forecasting, usage disaggregation, start & stop...
    Python Pycharm / VSCode Github AWS
  • MHIS
    Data Scientist
    WINE AND SPIRITS
    October 2019 - February 2020 (5 months)
    Paris, France
    - Extraction of data from vineyard capture
    - Identification of plots using the clustering method
    - Comparison of different contouring methods
    - Calculation of indicators for analyzing vine status
    - Extraction and export of data on the PROD Cloudera environment (HDFS)
    Python Pycharm CentOS
  • Europcar
    Data Scientist
    AUTOMOBILE
    July 2019 - October 2020 (1 year and 3 months)
    Paris, France
    - POC "One Group Customer"
    - Development / Industrialization of KPI calculations within the "Customer Knowledge" department
    Dataiku SQL PySpark Google Data Studio

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Education

  • Master in Electronics, Electrical Engineering and Automation
    Université Paul Sabatier
    2010
  • Ph.D. in Optimization and Applied Statistics
    Université de Technologie de Troyes
    2013
    Cette thèse traite de la détermination de classes de systèmes selon leur mode de vieillissement dans l’objectif de prévenir une défaillance et de prendre une décision de maintenance. L’évolution du niveau de dégradation observée sur un système peut être modélisée par un processus stochastique paramétré. Un modèle usuellement utilisé est le processus Gamma. De plus, on s’intéresse au cas où tous les systèmes ne vieillissent pas identiquement et le mode de vieillissement est dépendant du contexte d’utilisation des systèmes appelé ensemble de covariables. Il s’agit alors de regrouper les systèmes vieillissant de façon analogue en tenant compte de la covariable et d’identifier les paramètres du modèle associé à chacune des classes

Certifications

  • Dataiku DSS L2 Advanced
    Dataiku
    2019
  • AWS Certified Cloud Practitioner
    AWS
    2022

Skill set (30)

Categories