Sarath Tangudu

Report Development

• Developed and maintained complex servicing report for Auto Lending using SQL, SAS, Oracle, PowerBI and enterprise data warehouse sources, ensuring alignment with business requirements and reporting standards.
• Collaborated with business stakeholders to gather requirements, define KPIs and translate complex business needs into scalable reporting solutions
• Supported ALFA migration initiatives by analyzing reporting impacts, reviewing data mappings and successful implementation of revised logic
• Executed detailed source code reviews, including validation of joins, cardinality, transformation logic, calculations, filtering criteria, and reporting methodologies to ensure adherence to business requirements
• Performed data quality assessments, reconciliation testing, end-to-end validation of report outputs ensure data accuracy, completeness, and compliance with governance standards.
• Promoted adoption of AI-enabled development and validation practices, helping teams streamline repetitive task and focus on higher values activities
• Built strong relationships with business partners and technology teams, enabling effective collaboration throughout report development, testing and implementation.

• Designed and implemented machine learning models to enhance customer segmentation strategies.
• Led cross-functional teams to develop data-driven solutions improving operational efficiencies.
• Conducted advanced statistical analyses to inform strategic decision-making processes.
• Mentored junior data scientists, fostering skill development and best practices in analytics.

• Led execution of monthly Pre-Approved Direct Mail (PADM) campaign optimization model for credit card offers, monitoring upstream models (NCR, Balance, Revenue, Risk) to implement customized risk constraints, reducing losses by 10%.
• Developed LTV model for Merrill Edge Advisory Customers (MEAC) to enhance sales operations effectiveness.
• Mentored junior team members, fostering their growth and development into proficient data scientists.

• Developed a Multiple Linear Regression Model for Predicting the Residual Value of Used Fleet, used by the fleet acquisition planning tool.
• Responsible for the model development and quarterly execution of Customer Led Experience – Behavioral Cluster Analysis Model, targeted at customized marketing.
• Successfully deployed Fleet Demand Forecasting application to support fleet acquisition and also responsible for weekly execution. Reduced the MAPE to 5%.
• Increased the rental revenue by 9% through customized targeting, using Customer Lifetime Value model.
• Developed an optimization tool to aid Fleet Operations in selecting the Auction sale location. Estimated annual savings are $1M.