beauty Simora

Allen, TX

Phone: 214-***-**** Email: ad36hc@r.postjobfree.com

LinkedIn: https://www.linkedin.com/in/beauty-simora-31842b111/ GitHub: https://github.com/bsimms0909

Summary

A data-savvy finance professional with over a decade of experience. Skilled in extracting insights from data to drive informed decisions and contribute to organizational goals. Proficient in managing diverse responsibilities such as customer transactions, loan processes, and data management.

Technical Skills: Data exploitation and analysis Data Querying Data Modelling Data Visualization Data Storytelling and Reporting

Tools: Microsoft Excel Python SQL Tableau Jupyter Notebooks VBA Scripting Pandas

Databases: MySQL PostgreSQL Microsoft SQL Server SQLite MongoDB Google Colab

Relevant Experience

Customer Solutions Analyst: 2023-current

Ally Financial

Utilize data analysis techniques to process a minimum of 50 electronic payments daily, ensuring not only 100% adherence to regulations but also identifying trends and anomalies in financial information to optimize payment processes.

Effectively collaborate with team members to resolve payment issues, leveraging data analysis to achieve a remarkable 95% resolution rate and uncover insights for process improvement.

Conduct daily data quality checks, employing advanced data analysis methods to guarantee a remarkable 99.9% compliance with data security standards and proactively identify potential risks.

Achieve a perfect accuracy rate of 100% in verifying and reconciling payment requests, employing rigorous data analysis process to ensure precision and reliability in financial transactions.

Identify and rectify loan data discrepancies within 24-48 hours by employing techniques to efficiently analyze large datasets and pinpoint inaccuracies promptly.

Ensure 100% compliance with data confidentiality and security protocols by employing through analysis to assess vulnerabilities and implement strong security measures.

Implement advanced data analysis techniques to identify trends and anomalies in financial information, providing valuable insights for decision-making and strategic planning.

Mortgage Data Analyst: 2021-2022

Pentagon Federal Credit Union

Reviewed and verified a minimum of 15 loan applications and supporting documentation per day, achieving a 98% accuracy rate, while applying data analysis principles.

Assessed the creditworthiness of applicants based on established lending guidelines and criteria, resulting in a 20% reduction in default rates due to data-driven insights.

Analyzed financial statements, credit reports, and other documentation using data analysis techniques to determine the level of risk associated with each loan, resulting in a 15% improvement in risk assessment accuracy.

Ensured compliance with regulatory requirements, internal policies, and industry standards through data-driven evaluations, achieving a 100% compliance rate.

Utilized data analysis to verify and analyze the applicant's income, assets, and liabilities to assess their ability to repay the loan, resulting in a 25% reduction in delinquency rates.

Mortgage Data Analyst: 2021-2022

Chevron Credit Union

Assessed the creditworthiness of an average of 15 loan applications daily, consistently maintaining a stellar accuracy rate of 98%.

Thoroughly analyzed a range of financial statements and credit reports, ensuring a high accuracy rate of 98%. Conducted 6 compliance evaluations per month, consistently meeting regulatory requirements with a perfect compliance rate.

Precisely calculated all income types, achieving a flawless accuracy rate of 100% every day.

Collaborated closely with team members to enhance workflow efficiency, integrating data analysis techniques for process optimization, leading to a notable 15% improvement in overall workflow efficiency.

Mortgage Data Analyst: 2020-2021

Amerisave

Reviewed and analyzed loan applications, resulting in reduced default rates and improved risk assessment accuracy.

Used various analytical methods to review and verify an average of ten loan applications and supporting documentation daily, resulting in a 95% accuracy rate.

Calculated all income types, including W2 and self-employed income, utilizing quantitative analysis, resulting in a 25% reduction in income calculation discrepancies.

Analyzed loan risks, identified an average of five instances of potential fraud weekly, and made loan eligibility decisions using data-driven insights, leading to a 20% decrease in fraudulent loan approvals.

Ensured compliance with regulatory standards, company policies, and guidelines through data-driven evaluations, achieving a 99% compliance rate.

Documented and communicated reasons for approval or rejection, incorporating data-driven insights, resulting in a 30% increase in transparency and understanding among stakeholders.

Research Analyst: 2012-2021

Exeter Finance

Investigated disputes, and generated reports, leading to a 20% improvement in processing efficiency.

Identified 85% of payment trends through trend analysis.

Generated 15 actionable insights from payment activity reports per month.

Monitored payment processing trends accurately 100% of the time.

Assisted in the development and implementation of new processing systems, incorporating data analysis for system optimization, resulting in a 15% increase in processing system efficiency.

Successfully implemented payment processing systems/technologies within the specified timeline, leveraging data analysis to assess system requirements, optimize processes, and ensure seamless integration.

Data Analytics and Visualization Projects

A Comparative Study of Homeless Populations in Texas and California

[GitHub Repository] (https://github.com/ajturner3/Project-1)

Conducted a comparative study of homeless populations in Texas and California, utilizing Excel, Jupyter Notebook, Python, Pandas, and Matplotlib.

Crowdfunding ETL (Extract, Transform, Load) Project

[GitHub Repository] (https://github.com/elluis1001/Crowdfunding_ETL)

Led a crowdfunding ETL project, managing data extraction and transformation using Python, PostgreSQL, and ERD modeling of the tables.

Electric Vehicle Charging Stations Project

[GitHub Repository] (https://github.com/elluis1001/Group-3-Project)

Worked on the data visualization project, utilizing Python, Pandas, Requests, SQL Alchemy, Flask, Matplotlib, as well as JavaScript with D3.js and Plotly. Data was stored and queried from SQLite.

Diabetes Prediction using Machine learning Model Project

[GitHub Repository] (https://github.com/nidhi0684/Project4-DiabetesPrediction)

The objective is to predict the probability of being diabetic or non-diabetic utilizing Google Colab. The project utilized CSV data, PySpark, Pandas, Matplotlib, machine learning libraries, and Flask for deployment.

Education

University of Texas, Austin, TX: Data Analysis and Visualization

Collin College, Plano, TX: Associate in Arts

Pitman’s Examination Institute, London, UK: Computer Studies, Business Studies, English for Business Communications

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