Jobs

    Chief Data Scientist-Full-Time - Greater Toronto Area, Canada - AML Monitor

    AML Monitor
    AML Monitor Greater Toronto Area, Canada

    3 weeks ago

    Default job background
    Full time
    Description

    AML Monitor.

    • Data Analysis and Interpretation: Analyze large datasets to identify trends, patterns, and correlations, and translate findings into actionable insights.
    • Machine Learning Development: Develop and deploy machine learning models for predictive analysis, optimization, and other business applications.
    • Statistical Modeling: Design and implement statistical models to solve complex business problems and improve operational efficiency.
    • Data Visualization: Communicate findings effectively through data visualization techniques, such as charts, graphs, and dashboards.
    • Collaboration: Collaborate with cross-functional teams to understand business requirements, provide analytical support, and deliver solutions.
    • Quantitative Analysis: Utilize quantitative analysis, experimentation, data extraction, and data presentation to formulate strategies for various financial products.

    Team Building: Collaborate with cross-functional and Product, Engineering, and engineering teams to support, advise, and implement product investment and strategy decisions.

    Role Specific Technical Competencies

    • Master's degree in Computer Science, Computing engineering, Mathematics, Finance, Commerce or a related field.
    • Advanced skills in Information Technology, certification or diploma in Information Technology.
    • 3+ years of experience in Data Science
    • Data science experience ideally in ESG domain
    • Solid SQL skills for querying relational databases (e.g., SQL Server)

    Functional Requirement:

    • Proficiency in producing concise, comprehensible code adhering to prevailing industry benchmarks and practices.
    • Comprehensive understanding of both theoretical and practical aspects of Machine Learning principles.
    • Capability to identify and address data biases, proposing and implementing suitable mitigation strategies.
    • Demonstrated adeptness in interpersonal communication, effectively articulating intricate technical concepts to non-technical audiences.
    • Openness to diverse viewpoints and perspectives, fostering an inclusive and collaborative work environment.

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