Fraud Specialized Analytics Senior Analyst

• 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. Ownership and management of fraud models, risk appetite execution and defect analysis.
• 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.
• Collaborate closely with technology teams, fraud analytics, and business partners to align on data strategies, stay updated on industry trends, and proactively identify potential and existing fraud risks.
• 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.
• 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 with best practices in fraud risk management.
• Generate and manage regular and ad-hoc reporting to enable effective monitoring and identification of emerging trends.

• 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.
• Strong Technical Skills:

• 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.

• Data Engineering: Experience with building and optimizing data pipelines, 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-Solving: Strong problem-solving skills with the ability to think critically and creatively in a fast-paced environment.
• Regulatory Compliance: Familiarity with regulatory requirements and best practices related to fraud modeling and risk management.
• Multi-Tasking and Deadline Management: Demonstrated ability to manage multiple projects and priorities simultaneously while meeting tight deadlines.
• Attention to Detail: High level of attention to detail and precision in data analysis, model development, and reporting.
• Intellectual Curiosity: Strong intellectual curiosity and eagerness to stay updated with the latest developments in data science, machine learning, and fraud detection techniques.

Citigroup