Job
Description
As a Specialized Analytics Sr. Analyst at Citi's Fraud Analytics team, you will play a crucial role in supporting Citis North American Cards business by developing and validating fraud models. Your responsibilities will include: - Partnering closely with Strategy partners and Model Risk Management validators to manage interactions across the model lifecycle, from validation to ongoing performance evaluation and annual reviews. - Leading model development, validation, and testing of fraud detection models using tools like Python, H2O, and conducting statistical analysis, data validation, and model performance evaluation. - Documenting data requirements, processing, cleaning, and exploratory data analysis, potentially utilizing statistical models, algorithms, and data visualization techniques. - Ensuring compliance with regulatory requirements and industry best practices in fraud detection and model validation. - Compiling and analyzing large complex data sets using tools like Hive and SQL to predict, improve, and measure key business outcomes. - Having a specialization in the fraud or risk domain is preferred. - Assessing risk appropriately in business decisions, considering the firm's reputation and complying with laws and regulations. Qualifications required for this role: - 8+ years of hands-on analytical experience. - 5+ years of experience in statistical analysis with proficiency in Python, Hive, Spark, or SAS. - Strong quantitative, analytical, and problem-solving skills with knowledge of probability theory, statistics, mathematical finance, and numerical methods. - Proficiency in statistical analysis and model validation techniques, including Logistic Regression, Gradient Boosting, Random Forests, and evaluation techniques like ROC curves, precision-recall, etc. - Familiarity with regulatory requirements and guidelines related to risk model validation. - Strong communication, interpersonal, project management, and organizational skills. - Ability to work independently, handle large volumes of data, and a risk and control mindset. Education requirements: - Masters/Bachelors/PhD degree in Statistics, Economics, Finance, Mathematics, or a related quantitative field; Engineering/MBA background from a premier institute. This job description offers a detailed overview of the responsibilities and qualifications expected for the role. Other duties may be assigned as required. As a Specialized Analytics Sr. Analyst at Citi's Fraud Analytics team, you will play a crucial role in supporting Citis North American Cards business by developing and validating fraud models. Your responsibilities will include: - Partnering closely with Strategy partners and Model Risk Management validators to manage interactions across the model lifecycle, from validation to ongoing performance evaluation and annual reviews. - Leading model development, validation, and testing of fraud detection models using tools like Python, H2O, and conducting statistical analysis, data validation, and model performance evaluation. - Documenting data requirements, processing, cleaning, and exploratory data analysis, potentially utilizing statistical models, algorithms, and data visualization techniques. - Ensuring compliance with regulatory requirements and industry best practices in fraud detection and model validation. - Compiling and analyzing large complex data sets using tools like Hive and SQL to predict, improve, and measure key business outcomes. - Having a specialization in the fraud or risk domain is preferred. - Assessing risk appropriately in business decisions, considering the firm's reputation and complying with laws and regulations. Qualifications required for this role: - 8+ years of hands-on analytical experience. - 5+ years of experience in statistical analysis with proficiency in Python, Hive, Spark, or SAS. - Strong quantitative, analytical, and problem-solving skills with knowledge of probability theory, statistics, mathematical finance, and numerical methods. - Proficiency in statistical analysis and model validation techniques, including Logistic Regression, Gradient Boosting, Random Forests, and evaluation techniques like ROC curves, precision-recall, etc. - Familiarity with regulatory requirements and guidelines related to risk model validation. - Strong communication, interpersonal, project management, and organizational skills. - Ability to work independently, handle large volumes of data, and a risk and control mindset. Education requirements: - Masters/Bachelors/PhD degree in Statistics, Economics, Finance, Mathematics, or a related quantitative field; Engineering/MBA background from a premier institute. This job description offers a detailed overview of the responsibilities and qualifications expected for the role. Other duties may be assigned as required.
