QUANTITATIVE ANALYST
Aggregate and engineer data from multiple sources in preparation for model development. Clean and validate datasets for compatibility with quantitative frameworks and identify KPI variables. Apply d…
QUANTITATIVE ANALYST
Aggregate and engineer data from multiple sources in preparation for model development. Clean and validate datasets for compatibility with quantitative frameworks and identify KPI variables. Apply domain expertise to assess ideal modeling heuristic. Research and test various quantitative and statistical analytical methods to develop optimal model from the available data, including determining applicability of standard or novel machine learning algorithms. Parse and present model results in simple human-understandable format.
- Managed initial design of the post-implementation analysis of the re-engineered AML department customer risk rating model. Aggregated data from 4.5m transaction records over 16-month period.
- Computed rolling average statistics of transaction volume across 15 transaction groups for each unique customer ID to calculate regression targets for model tuning.
- Tested model structures for parameter optimization prior to company liquidation announcement.
- Leveraged familiarity with data warehouse to develop dashboard that enabled AML department to monitor custodial and non-custodial funds transfers. Utilized complex key-mapping to program SQL query that populated relevant transaction tables and volume visualizations into Power BI report.
- Engineered a custom variant of forecasting algorithm developed from independent mathematical research to generate fraud alert count predictions for final month of operations.
Managed initial design of the post-implementation analysis of the re-engineered AML department customer risk rating model.
Aggregated data from 4.5m transaction records over 16-month period.
Computed rolling average statistics of transaction volume across 15 transaction groups for each unique customer ID to calculate regression targets for model tuning.
Tested model structures for parameter optimization prior to company liquidation announcement.
Leveraged familiarity with data warehouse to develop dashboard that enabled AML department to monitor custodial and non-custodial funds transfers.
Utilized complex key-mapping to program SQL query that populated relevant transaction tables and volume visualizations into Power BI report.
Engineered a custom variant of forecasting algorithm developed from independent mathematical research to generate fraud alert count predictions for final month of operations.
Contributed to developing the company’s data warehouse by compiling data from five different databases including FIS Cognos and Verafin.
Engineered requirement-specific business intelligence reporting from the company’s databases, including Power BI Report development and exporting ad-hoc transaction record requests from other business units.
Managed 400+ user data access permissions across company, documenting activation and deactivation requests and distributing login credentials.
Oversaw four-week project meeting with business units discussing correct categorizations of transactions across seven payment types to comply with 2021 Federal Reserve Payments Study.
Built SQL queries around specifications and manually analyzed data not imported to data warehouse.
Calculated final aggregates and submitted all verifiable data to Federal Reserve web portal.
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