Payroll Case Relevance Prioritization Prototype
Dec
2024
Problem / Purpose
The Payroll Operations Team was missing critical information buried in Salesforce cases due to lack of labeling, causing a high error rate in payroll processing. Teams were overwhelmed and unable to manually flag relevant cases.
Solution
Proposed a multi-phase project to automate case review using Snowflake Cortex’s SUMMARY function and keyword flagging. Built a prototype report that scored and sorted cases by potential relevance based on flagged terms in email content, subjects, and descriptions. Report included summaries, filters by pay date, and deep links to case details. Future phases included iterative refinement of keywords and training an ML model on labeled case data.
Key Achievements / Impact
Delivered a working prototype to prioritize high-risk payroll cases with automated summaries and scoring logic. Though full implementation was paused due to shifting company priorities, the solution was positively received and seen as highly impactful by the Operations Team.
Key Technologies / Tools Used
Snowflake, SQL, Snowflake (Cortex Generative AI Tools), Salesforce, Data Product Design, DBT, Dagster, End-to-End Data Pipeline Design, Microsoft SQL Server (MSSQL), Process Improvement, Text Mining
Role
Data Scientist (company project)
ProService Hawaii