Specialized Analytics Lead Analyst

As part of Citi’s Financial Crimes and Fraud Prevention - Modeling and Data organization, this role leverages advanced machine learning tools and data mining techniques to identify and combat fraud. A key focus of the role is on data and feature engineering; transforming raw and complex datasets into optimized inputs for developing high-performance fraud models. The role will be responsible for developing and implementing sophisticated fraud models aimed at preventing and mitigating fraud risks across the full fraud lifecycle including application fraud, synthetic ID fraud, account takeover, and evolving fraud attack methods. The ideal candidate will bring a strong technical background in data processing, feature engineering, and data manipulation, playing a pivotal role in enabling the development of effective and scalable fraud models. The role requires expertise in extracting and engineering key features from large datasets, ensuring that models are not only accurate but also resilient against emerging fraud patterns. The role will work closely with technology teams, fraud analytics, and various business partners to stay informed about business and technology shifts, identifying both potential and existing fraud impacts. Technical proficiency in model optimization, algorithm development, and real-time analytics is essential for enhancing fraud prevention efforts. Responsi [... source excerpt omitted ...] feature selection, hyperparameter tuning, and ongoing performance monitoring, ensuring models remain adaptive to new fraud tactics. Support model deployment and integration into production systems, ensuring seamless real-time fraud detection and efficient feedback loops for continuous model improvement. Evaluate and select appropriate machine learning algorithms and tools based on specific fraud detection needs and data characteristics. Engage in cross-functional initiatives to enhance data quality and governance, improving overall fraud prevention capabilities. Participate in model validation and testing processes to ensure compliance with regulatory standards and alignment w [... source excerpt omitted ...] pelines, ETL professes, and real-time data streaming for fraud detection solutions. Machine Learning Operations: Familiarity with model development, monitoring, and versioning in production environments. Analytics Skills: Strong ability to conduct exploratory data analysis (EDA) and identify actionable insights from large datasets to drive model development. Collaboration: Proven track record of working cross-functionally with technology, analytics, and business teams to implement and optimize fraud prevention strategies. Communication: Ability to translate complex technical findings into clear, actionable insights for non-technical stakeholders and business leaders. Problem-S