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    Fraud Strategy Data Scientist - San Jose, CA, United States - AIT Global Inc

    AIT Global Inc
    AIT Global Inc San Jose, CA, United States

    1 day ago

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    Description
    Fraud Strategy Data Scientist
    We are looking for a talented, enthusiastic, and dedicated person to support the Fraud Risk Strategy team. The incumbent will be responsible for supporting key projects associated with fraud detection, risk analysis and loss mitigation.

    This position requires a person who has experience with performing analytics, refining risk strategies, and developing predictive algorithms preferably in the risk domain.


    We'd love to chat if you have:


    Minimum 2 years of experience in risk analytics, data analysis, and data science within relevant industry experience in eCommerce, online payments, user trust/risk/fraud, or investigation/product abuse.

    Bachelor's degree in computer science, Engineering, Mathematics, Statistics, Data Mining or related field or equivalent practical experience
    Experience using statistics and data science to solve complex business problems.
    Proficiency in SQL, Python, Excel including key data science libraries.
    Proficiency in data visualization including Tableau.

    Ability to clearly communicate complex results to technical experts, business partners, and executives including development of dashboards and visualizations, ie Tableau.

    Comfortable with ambiguity and yet able to steer analytics projects toward clear business goals, testable hypotheses, and action-oriented outcomes.

    Demonstrated analytical thinking through data-driven decisions, as well as the technical know-how, and ability to work with your team to make a big impact.

    Desirable to have experience or aptitude solving problems related to risk using data science and analytics.

    Bonus:
    Experience with AWS, knowledge of fraud investigations, payment rule systems, working with ML teams, fraud typologies

    Design rules to detect/mitigate fraud.
    Develop python scripts and models that support strategies.
    Set strategy for different risk types.

    Work closely with team members and stakeholders to consult, design, develop, and manage fraud strategies and rules that not only solve emerging fraud trends but also provide a great experience to end customers.

    Utilize data analysis to design and implement fraud strategies.

    Collaborate with cross-functional stakeholders including product managers and engineering teams to deploy data-driven fraud solutions that operate at scale and in real time for end customers.

    Development of dashboard and visualizations to track KPI of fraud strategies implemented.

    Data analytics and models
    Project Management
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