Business Analytics Lead Analyst

Citi • Irving, TX

3d • $153,200 - $168,558 • Hybrid

About The Position

Citibank, N.A. seeks a Business Analytics Lead Analyst for its Irving, TX location. Duties: Analyze customer and transactional data to identify emerging fraud trends and develop or improve fraud strategies. Evaluate output of data analysis in statistical tools and build decision trees to drive fraud alerting rules. Review and develop dashboards and communication of fraud results using advanced visualization techniques. Develop impact projections of various solutions or new processes on fraud detection, customer impact rates, operational capacity planning, and loss recoveries. Implement models into production by determining optimal score cut offs to maximize key business target metrics on fraud detection, customer impact rate, dispute resolution, and operational capacity planning. Create strategies/rules to detect anomalous trends utilizing statistical and advanced data science/Machine Learning techniques. Develop tactical and strategic Management Information Systems (MIS) dashboards using tools like Tableau with high visibility for key stakeholders. Provide actionable insights by leveraging data analytics and reporting. Partner with cross-functional teams to design effective strategies to detect Fraud. Drive the end-to-end testing approach – including test case documentation, review and monitoring of rule performance, fine-tune rules – and strategy implementation. Increase sophistication of anomaly detection analytics and develop new detection models and analytical solutions. A telecommuting/hybrid work schedule may be permitted within a commutable distance from the worksite in accordance with Citi policies and protocols.

Requirements

• Bachelor’s degree, or foreign equivalent, in Business Analytics, Computer Engineering or a related field, and five (5) years of experience in the job offered or in a related occupation.
• Utilizing analytical, statistical, and programming skills, including SAS and SQL programming to collect, analyze, and interpret large transaction data in a Big Data environment
• Analyzing customer, transactional and behavioral data to develop machine learning models using Python to predict fraudulent transactions
• Performing complex data mining and analysis on Big Data to identify fraud trends and patterns
• Evaluating output of data analysis in statistical tools to drive fraud alerting rules and developing tactical and strategic reports using visualization tools with high visibility for key stakeholders
• Driving collaboration across different teams such as Model Risk Management and Legal team to validate the results and stability of models before being pushed to production.

Responsibilities

• Analyze customer and transactional data to identify emerging fraud trends and develop or improve fraud strategies.
• Evaluate output of data analysis in statistical tools and build decision trees to drive fraud alerting rules.
• Review and develop dashboards and communication of fraud results using advanced visualization techniques.
• Develop impact projections of various solutions or new processes on fraud detection, customer impact rates, operational capacity planning, and loss recoveries.
• Implement models into production by determining optimal score cut offs to maximize key business target metrics on fraud detection, customer impact rate, dispute resolution, and operational capacity planning.
• Create strategies/rules to detect anomalous trends utilizing statistical and advanced data science/Machine Learning techniques.
• Develop tactical and strategic Management Information Systems (MIS) dashboards using tools like Tableau with high visibility for key stakeholders.
• Provide actionable insights by leveraging data analytics and reporting.
• Partner with cross-functional teams to design effective strategies to detect Fraud.
• Drive the end-to-end testing approach – including test case documentation, review and monitoring of rule performance, fine-tune rules – and strategy implementation.
• Increase sophistication of anomaly detection analytics and develop new detection models and analytical solutions.

Benefits

• medical, dental & vision coverage
• 401(k)
• life, accident, and disability insurance
• wellness programs
• paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level