freelance
Senior Data Scientist Reference number: 616
Published: 20-11-2018, 14:36
Region: Brussels
Sector: Telecommunication
Education: PhD or Master’s degree in a quantitative field (Artificial Intelligence, Computer Science, Statistics, Mathematics, etc.)
Work experience: Minimum 2 years of relevant work experience
Job type: Contract
Job description
Within the Company Corporate Strategy & Market Insights department, you will be part of the Operational Efficiency Analytics team and focus on the application of data science methods to improve the internal processes of the Company, in order to achieve better customer experience and cost savings.
What you’ll do
  • Translate business requests into data requirements, extract the required structured and unstructured data from the Proximus data lake, data warehouse and other data sources (e.g. operational systems) and prepare large-scale datasets for modelling, using Python/Spark/SQL/R.
  • Identify high-value use cases through data exploration and visualization.
  • Develop predictive/prescriptive models using machine learning/statistical methods and build prototypes demonstrating the value of these models.
  • Deploy prototypes to production, with support of IT, to obtain reliable, scalable systems.
  • Present your results and discuss them with multifunctional project teams.
  • Work in close collaboration with business experts (for requirement gathering, data understanding, feature engineering, result validation, etc.) and IT (for deployment to production).
Profile
  • Team player with strong communication and presentation skills
  • Able to manage data analytics projects in an autonomous way
  • Experience in telecommunications sector and/or operational efficiency domain is a plus
Required Skills
  • Strong knowledge of machine learning algorithms and statistical analysis
  • Strong hands-on experience with data science technologies, incl.:
    Data science: Python
    Big data: Spark, Hadoop
    Relational databases: SQL
    Experience with real-time streaming is a plus
  • Experience with complete data science lifecycle, incl.:
    Business requirements gathering
    Data understanding, preparation, visualization
    Data exploration, identification of high-value use cases
    Modelling (incl. feature engineering, model fitting, etc.)
    Deployment to production, reporting, maintenance
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