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About Me

I’m a data and accounting professional with more than twenty years of Big Four experience developing technology-driven solutions for financial and tax teams. I am completing a Master’s in Applied Data Science at USC (Dec 2025), which builds on my professional background and strengthens my skills in Python, SQL, Spark, Airflow, and AWS for large-scale financial data processing.

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My work centers on improving the accuracy and reliability of accounting and reporting systems through scalable data pipelines, automated reconciliations, anomaly detection, and retrieval-based analytics. I also apply modern AI tools such as LangChain and LangGraph to support advanced analysis and decision-making.

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Thank you for visiting. You can download my resume below or explore my background and experience on this page.

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Education

Master of Science in Applied Data Science - Expected 12/2025

University of Southern California (USC) • GPA: 3.50

Los Angeles, CA

Master of Business Administration in Finance and Strategic Management

Claremont Graduate University

Claremont, CA​

Bachelor of Science in Biological Science
Bachelor of Arts in Social Science

University of California, Irvine

Irvine, CA​

Work Experience

May 2024 - Present

January 2010 - April 2024

Professional Sabbatical

Data Professional – Graduate Study and Technical Development

  • Took a planned career pause to support my daughter’s start in school while completing graduate studies and expanding technical proficiency in data systems and analytics.

  • Developed hands-on projects in recommendation systems, time series modeling, and retrieval-driven data pipelines reflecting current advances in intelligent and agentic technologies.

Ernst & Young

Senior Manager, Tax Technology and Transformation Services

  • Reduced manual data preparation time by 50% through automated pipelines, freeing teams to focus on higher value financial analysis and review.

  • Cut end-to-end review cycle times by 3 days by implementing standardized reconciliation logic across tax and accounting datasets.

  • Improved accuracy of tax data processing by 20% through automated anomaly detection, validation checks, and cross-system reconciliation workflows.

  • Enabled faster quarter-end reporting by building ingestion pipelines capable of processing millions of rows of structured client data.

  • Managed multiple parallel workstreams (client delivery, automation development, quality reviews, training, and senior-level presentations) while maintaining accuracy and deadlines.

  • Designed and implemented scalable ETL pipelines supporting ingestion, validation, and transformation of global tax and accounting datasets.

  • Oversaw SaaS and application development projects integrating tax and accounting compliance solutions with large-scale financial datasets.

  • Partnered with clients to scope and deliver custom automation tools for tax data analysis, reporting, and GAAP aligned reconciliation workflows.

  • Led development of Python and SQL based automation tools that improved data accuracy and accelerated multi-stage review cycles.

  • Improved financial data quality by introducing automated validation, reconciliation, and variance detection logic across high volume financial reporting flows.​

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Manager, Tax Technology Transformation Services

  • Improved team productivity by 25% by developing reusable SQL scripts, standardized templates, and automated data prep workflows.

  • Reduced reconciliation effort by 20 hours per cycle through SQL based variance detection and exception identification logic.

  • Led a team of 10 consultants on data intensive tax and accounting engagements requiring SQL, database design, and analytical scripting.

  • Integrated structured client data from multi-source systems into reporting and analytics platforms, supporting downstream tax and financial workflows.

  • Championed technology driven process improvements that accelerated review cycles and strengthened data reliability.

  • Collaborated with cross-functional teams (IT, finance, tax, engineering) to standardize data ingestion, mapping, and reconciliation processes.

  • Accelerated data integration and mapping across financial systems, improving downstream reporting efficiency and consistency.

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Senior Consultant, Business Tax Advisory

  • Conducted interviews with SMEs and used quantitative analysis to support tax credit and deduction positions.

  • Managed full project lifecycle including budgeting, execution, and delivery.

  • Built analytical tools in Excel using pivot tables and charts to support findings.

Relevant Coursework

USC Master's Program

Information Retrieval and Search Systems

  • Constructed indexing pipelines and ranking models; applied precision/recall metrics and retrieval techniques relevant to large-scale financial datasets.​

Machine Learning and Recommendation Systems

  • Implemented logistic regression, SVM, decision trees, ensembles, and collaborative filtering using PySpark.

Data Engineering and Architecture

  • Developed distributed data pipelines with Hadoop and Spark; optimized schema design and NoSQL integration (MongoDB, HBase, DynamoDB); reinforced skills foundational to AWS-based data engineering.

Applied Data Processing and Analytics in Python

  • Web scraping, object oriented design, time series, geospatial and text data analysis; emphasized data ethics, provenance, and automated analysis pipelines.

Skills

Languages: Python, Scala, SQL​

Cloud and Data Platforms: AWS (S3, Glue, Redshift), Databricks, MongoDB, MySQL, NoSQL Systems, SQL Server

Tools and Frameworks: Airflow, Jupyter Notebooks, LangChain, LangGraph, NumPy, Pandas, Power BI, PySpark, Scikit-learn, Spark, Tableau, XGBoost

Data Engineering Competencies: Data Governance, Data Pipeline Development, Distributed Processing, ETL/ELT Workflows, Scalable Data Architecture

Machine Learning Competencies: Anomaly Detection, Classification, Clustering, Feature Engineering, Model Evaluation, Regression, Time Series Analysis

AI System Competencies: Agentic AI Applications, Information Retrieval, Recommendation Systems, Retrieval-Augmented Generation (RAG)

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