Data Scientist

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Angajator: Freelancer IT
  • IT Software
  • Tip job: full-time
    Nivel job: 1 - 5 ani experienta
  • Actualizat la: 27.12.2017
    Scurta descriere a companiei

    First and foremost we believe in the people on this site. We believe in those who want to develop and grow, both small companies and IT professionals and we know that we can create something beautiful together. We believe in their dreams, we believe in your dreams and we intend to prove the quality of the Romanian IT industry.


    1. 2+ years’ experience working as a data scientist in the credit risk management department of a Bank / IFN or a consulting company (industry expertise in banking / IFN)
    2. Advance Degree in Economics, Statistics, Mathematics, or Decision Sciences
    3. Advance knowledge with one or more of the following statistical packages – R, Python, Matlab, SAS
    4. Advance database programming knowledge of SQL, PL/SQL
    5. Excellent verbal and written communication skills (including in English)


    For the role of a Data Analyst, we are looking for someone who has a good understanding of the business needs and objectives of a credit risk management (consumer based) department within a Bank / IFN, and can use advance data analytical techniques to provide business insights and solutions.

    1. Follow a structured approach in solving business objectives using data analytics
    • Work with a hypothesis based methodology of identifying problems and working towards its solution
    • Proactively work towards improving existing data models, as well as introducing new models based on industry best practices
    • Identify key data sources and obtain access/feeds/files necessary to perform data analytics
    • Use data mining techniques to prepare the data for analysis
    • Apply statistical techniques to measure results and show key trends, identify causal impact and attribution, analyze experiments, and model and predict future performance of loan portfolio
    • Present key findings to the team

    2. Some of the models that the candidate could help create, include
    • Continuous improvement of existing credit scoring model and development of new credit scoring models
    • Stress testing of credit risk using sensitivity and scenario analysis
    • Risk based pricing models • Develop models for Loss Given Default (LGD) and Exposure at Default (EAD)

    3. Periodic monitoring of existing financial KPIs and helping put in place new KPIs related to the performance of the loan portfolio