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Leveraging advanced machine learning tools and data mining techniques to identify and combat fraud for Citi's Financial Crimes and Fraud Prevention organization.

Requirements

• Proficiency in programming languages such as Python, R, or SQL for data manipulation, feature engineering, and model development.
• Strong experience with data processing tools and libraries (e.g., Pandas, Numpy, PySpark) for handling large and complex datasets.
• Deep understanding of machine learning algorithms (e.g., decision trees, gradient boosting, neural networks, natural language processing) and statistical modeling techniques used for fraud detection
• Expertise in feature engineering, including creating, selecting, and refining features to improve model accuracy and performance.
• Experience with building and optimizing data pipelines, ETL professes, and real-time data streaming for fraud detection solutions.
• Familiarity with model development, monitoring, and versioning in production environments.
• Strong ability to conduct exploratory data analysis (EDA) and identify actionable insights from large datasets to drive model development.

Responsibilities

• Lead data and feature engineering efforts to extract, transform, and prepare high-quality data inputs for fraud model development, focusing on identifying key attributes that drive accurate fraud detection.
• Build predictive models and machine-learning and AI algorithms with large amounts of structured and unstructured data.
• Design, develop, and implement advanced machine learning models to detect and prevent fraud across the entire lifecycle, including application fraud, synthetic ID fraud, account takeover, and evolving attack schemes.
• Utilize advanced data processing techniques to manage large, complex datasets, including data cleaning, normalization, and augmentation, ensuring robust model performance.
• Conduct comprehensive exploratory data analysis (EDA) to uncover hidden patterns, trends, and anomalies that can inform model development and feature engineering.
• Continuously optimize and refine fraud models through 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.

Other

• Bachelor’s Degree required in statistics, mathematics, physics, economics, or other analytical or quantitative discipline. Master's Degree or PhD preferred.
• 3+ years in data science, machine learning, or advanced analytics.
• Proven track record of working cross-functionally with technology, analytics, and business teams to implement and optimize fraud prevention strategies.
• Ability to translate complex technical findings into clear, actionable insights for non-technical stakeholders and business leaders.
• Strong problem-solving skills with the ability to think critically and creatively in a fast-paced environment.