Quantitative analytics professional with experience in data analysis, data profiling, data cleaning, ETL, reporting, and dashboard development across banking, consulting, and higher education environments. Skilled in Pyt…
Quantitative analytics professional with experience in data analysis, data profiling, data cleaning, ETL, reporting, and dashboard development across banking, consulting, and higher education environments. Skilled in Python, SQL, cloud platforms, data visualization tools, and statistical techniques, with experience improving data quality, fraud detection, and operational efficiency.
Conducted data analysis and profiling to identify credit risk data quality issues, leveraging SQL queries and statistical analysis techniques.
Reduced data errors and inconsistencies by 25%.
Implemented data analysis and advanced data mining methodologies using Python on GCP to create a regression model aimed at identifying risk-related attributes.
Reduced delinquency rates by 23%.
Deployed real-time risk prevention measures by integrating advanced validation techniques, including behavioral analytics.
Decreased identifying high-risk customers by 13%.
Transferred reports from Excel to Looker Studio and automated workflows.
Improved the team's operational efficiency by 32% and reduced manual efforts.
Conducted data analysis and profiling to identify credit risk data quality issues, leveraging SQL queries and statistical analysis techniques,
resulting in a 25% reduction in data errors and inconsistencies.
Implemented data analysis and advanced data mining methodologies using Python on GCP to create a sophisticated regression model aimed
at identifying risk-related attributes, leading to a notable 23% reduction in delinquency rates.
Deployed real-time risk prevention measures by integrating advanced validation techniques, including behavioral analytics, which
coordinated to a significant 13% decrease in identifying high-risk customers.
Transferred reports from Excel to Looker Studio and automated workflows, resulting in a commendable 32% enhancement in the team's
operational efficiency, reducing manual efforts
Conducted thorough research and analysis, employing Python for data cleaning and Exploratory Data Analysis (EDA) to uphold the quality and pertinence of variables associated with courses.
Coordinated with the UERU Team in the establishment of an online portal for students.
Enabled seamless course selection, profile management, and the efficient generation of over 450 reports.
Improved data integrity by implementing data cleaning process.
Engineered data processing in Snowflake for analyzing vast datasets and interpreting complex patterns.
Achieved a 25% improvement in fraud detection rates.
Developed performance metric charts in Tableau with drill-down capabilities, incorporating trend lines and predictive analytics directly into the dashboard.
Improved data communication and facilitated fraud anticipation.
Conducted ongoing reviews to monitor and adjust model outputs.
Adapted strategies to address evolving fraud patterns and ensured sustained high accuracy.
Collaborated with data engineers to implement an ETL process.
Executed data extraction through the creation of robust SQL queries on extensive relational databases.
Yielded a significant 15% reduction in prediction errors.
Designed and implemented ETL processes using SQL Server Integration Services (SSIS) to extract, transform, and load data from diverse sources into the data warehouse.
Enhanced storytelling by integrating custom visuals, including time-series line and area charts in Power BI.
Improved stakeholder comprehension of historical trends and future predictions by 32%.
Collaborated with Data Engineering and Software teams to deploy the forecasting model on AWS.
Enabled real-time prediction and integration into production.
Spearheaded the development and adherence to data governance policies.
Ensured data accuracy and reliability.
Led to a 27% reduction in processing time for forecasting models.
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