Data Scientist -Finance

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Employer: Inetum Romania
Domain:
  • IT Hardware
  • IT Software
  • Job type: full-time
    Job level: peste 5 years of experience
    Location:
  • BUCHAREST
  • Updated at: 05.02.2022
    Remote work: On-site
    Short company description

    About Inetum, Positive digital flow:

    Inetum is an 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. The Inetum group is committed towards all these players to innovate, continue to adapt and stay ahead. With its multi-expert profile, Inetum offers its clients a unique combination of proximity, a sectorial organization and solutions of industrial quality. Operating in more than 26 countries, the Group has nearly 27,000 employees and in 2020 generated revenues of €1,965 billion.

    Inetum Romania is an important player in the IT services and solutions market in our country, with over 15 years of activity. It is a stable, growing and profitable company with over 500 employees who provides IT consulting services, infrastructure and software development assistance, digital services implementation and support.

    In an Agile format, our teams work on cloud initiatives, application development, business intelligence, automation and digitalization projects that contribute to the profit and evolution of our clients. The diversity of our projects offers team members the opportunity for learning and growth. The company had a turnover of 20 million EURO in 2021.

    Requirements

    Strong level of experience in designing/developing industrial IT solutions.
    Strong experience in developing and deploying machine learning, deep learning, NLP solutions.
    Experience with common data science toolkits, ( Scikit, NumPy or R libraries) .
    Proficiency with any one NoSQL databases ( MongoDB, Cassandra, HBase)
    Experience in developing APIs, services using either C# or Java
    Good awareness on entire machine learning/ predictive modeling & implementation
    Hands on experience with SAS AML tool
    Very good knowledge of SAS Data and procedures
    Strong background in analytics / modeling
    Mastery of R / Python
    Strong analytical data science / machine learning knowledge
    Excellent understanding of machine learning techniques and algorithms,
    Experience in selecting features, building and optimizing classifiers using machine learning techniques.
    Prior experience with data visualization tools.
    Good knowledge on statistics skills
    Adequate presentation and communication skills to explain results and methodologies to non-technical stakeholders
    French basic-medium level (willing to learn and improve)
    Basic understanding of the banking industry is a plus
    .

    Responsibilities

    Ability to scan the tool thoroughly following the SAS rules, Raise alerts , optimize these scenarios, reduce false positives etc.
    Ability to communicate analyzes, especially by using visualization tools in a clear way with decision orientation
    Strengthening the regulatory and accounting requirements relating to the supervision and monitoring of risk models and the resulting issues
    Develop, process, cleanse and enhance data collection procedures from multiple data sources.
    Conduct & deliver experiments and proof of concepts to validate business ideas and potential value.
    Test, troubleshoot and enhance the developed models in a distributed environments to improve it's accuracy.
    Work closely with product teams to implement algorithms with Python and/or R.
    Design and implement scalable predictive models, classifiers leveraging machine learning, data regression.
    Facilitate integration with enterprise applications using APIs to enrich implementations
    Participate in the development and / or evolution of statistical models and machine learning applied to Financial Security
    Conducting an internal model review and calibration as well
    Produce and analyze model performance indicators
    Must be self-directed and comfortable supporting the data needs of multiple teams, systems and products., prospect of optimizing or even re-designing company’s data architecture to support next generation of products and data initiatives
    Model design, feature planning, system infrastructure, production setup and monitoring, and release management.

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