Data Scientist

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Employer: Vauban becomes Inetum
Domain:
  • IT Software
  • Job type: full-time
    Job level: 1 - 5 years of experience
    Location:
  • BUCHAREST
  • Updated at: 13.03.2021
    Short company description

    Inetum is an agile IT services company that provides digital services and solutions and a global group that helps companies and institutions to get the most out of digital flow.

    Vauban, the Romanian division of Inetum, is an important player in the IT services and solutions market in our country, with over 13 years of activity. Vauban has over 450 employees who provide, from the service centers in Bucharest, Pitesti and Constanta, IT consulting services, infrastructure and software development services, digital services, solutions for Smart City.

    Requirements

    Core skills:
    • Statistics (descriptive and inferential)
    • Machine learning algorithms (backed up by experience building production models on various types of data)
    • SQL for data preparation
    • Ability to present the model and pitch it to an outside, non-technical audience (in English)

    Desirable skills:
    • Programming languages (Python preferred)
    • Cloud platforms (Google Cloud Platform preferred)
    • Data visualization (Google Data Studio, Spotfire, Tableau, Power BI, Qlik etc.).

    Responsibilities

    Upstream of a project
    • Collaborate with various teams in order to imagine innovative analytical processes solutions and identify synergies between projects
    • Check the availability and quality of data sources
    • Analyze datasets and experiment new ways of uncovering valuable insight

    During the project phase
    • Participate in the project lifecycle and interact with key members on technical and business subjects
    • Analyze the business problem and understand it in detail
    • Data engineering and feature engineering
    • Create precise and efficient analytical models
    • Document and present the models and their parameterization
    • Develop and integrate models in production, from data preparation to prediction
    • Define and document the tests related to the model (re-training, drift)

    In non-project phase
    • Continuous learning
    • Peer validation and support of implementations from other data scientists
    • Involvement in Data acculturation (training, sharing insight)
    • Participation in the recruitment of new Data Scientists.