Collaborated with business stakeholders to define data requirements and use quantitative techniques to derive actionable insights.Determine the cost of operations by establishing standard costs; collecting operational data and by using research analytics and data modeling techniques.Identified financial status by comparing and analyzing actual results with plans and forecasts.Interpreted data on price, yield, stability, future investment risk trends, economic influences, and other factors affecting investment programs for Subprime deep subprime consumer finance project using SQL and SAS coding.Increased productivity by developing automated accounting applications; coordinating information requirements and performing the cost-benefit analysis.Maintained technical knowledge by attending educational workshops; reviewing publications and contributing to team efforts.Guided cost analysis process by establishing and enforcing policies and procedures; providing trends and forecasts; explaining processes and techniques; recommending actions.Improved financial status by analyzing results; monitoring variances; identifying trends; recommending actions to management.Reconciled transactions by comparing, correcting, and validating data.Recommended actions by analyzing and interpreting data and making comparative analysis; studying proposed changes in methods and materials using SAS programming.Created audit reports involving charts and graphs using Tableau dashboards and SAS, to illustrate revenue comparison for subprime deep prime lending.

Developed and executed advanced data analysis techniques to track customer journeys and marketing effectiveness in the finance sector.Collaborated with business stakeholders to define data requirements and use quantitative techniques to derive actionable insights.Applied classification, regression, and clustering algorithms to financial datasets, enhancing predictive capabilities and decision-making processes.Conducted in-depth financial data analysis to support the development and validation of loan and finance models.Implemented advanced data validation techniques to ensure the accuracy and reliability of financial datasets.Designed and automated financial reports and dashboards in Power BI to monitor key performance indicators (KPIs) related to loans.Utilized machine learning techniques and statistical methods to build predictive models for assessing loan risk and customer creditworthiness.Managed and optimized databases to streamline data extraction, transformation, and loading (ETL) processes for financial analysis.Collaborated with stakeholders to understand business requirements and translate them into effective data-driven solutions.Presented analytical findings and recommendations to senior management, enhancing strategic decision-making in the finance domain.Built 20+ Tableau live and interactive dashboards for real-time monitoring of brand awareness and sales force effectiveness, enabling quicker data-driven decisions and increased annual revenue by $500K.

I extracted, cleaned, and validated data using SQL and Python.Ensured data integrity and accuracy for strategic decision-making processes.Utilized Tableau for performance monitoring, trend analysis, and data visualization.Developed Python scripts to automate data extraction, transformation, and loading (ETL) processes.Improved efficiency by reducing processing time by 30%, enhancing data accessibility and reliability.Assisted in developing quantitative models for risk analysis and portfolio optimization.Provided data-driven insights to optimize investment strategies and minimize risks.Analyzed and interpreted complex datasets related to loan and finance, contributing to data-driven decision-making processes.Developed and maintained comprehensive reports and dashboards using Power BI and Tableau to track financial metrics, loan performance, and customer behavior.Performed ETL processes to ensure data integrity and accuracy, optimizing the data pipeline for financial data analysis.Utilized SQL and Python to conduct statistical analysis, identify trends, and forecast loan default rates.Collaborated with cross-functional teams to enhance data collection procedures and improve data quality for finance-related projects.