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• Own the month-end close for 3 entities with $180M in revenue; cut close time from 9 to 6 business days.
• Reconcile 60+ accounts a month in NetSuite and BlackLine; no audit adjustments in 2024 or 2025.
• Built a fixed-asset roll-forward in Excel that replaced manual tracking of 1,200 assets.
• Review the work of 2 staff accountants and run their quarterly training.

• Prepared journal entries, accruals and bank reconciliations for a $65M business.
• Moved invoice approval into NetSuite workflows, cutting late vendor payments by about 30%.
• Supported the first external audit after a private-equity acquisition.

• Audited manufacturers and distributors with $20M–$250M in revenue across 9 engagements.
• Tested revenue, inventory and payroll controls and wrote up findings for client management.

• Hire and onboard 40–60 seasonal and part-time staff a year; cut time to hire from 21 to 12 days.
• Introduced structured interviews with a 5-question scorecard for 3 store openings.
• Kept first-90-day turnover at 18% against a 27% district average.
• Moved schedules and time-off requests for 45 employees from paper to Workday.

• Onboarded 25 new hires a year; wrote a 2-week training checklist now used in 6 stores.
• Handled first-line employee relations and documented coaching for 30+ staff.

• Trained 8 new booksellers and ran the weekly schedule for a team of 14.

P&L of $310M · 9 distribution centers · 1,400 employees

• Lead 9 site general managers and a regional engineering team of 12.
• Rolled out a new warehouse management system to every site in 14 months with no missed client SLAs.
• Renegotiated national carrier contracts, saving $6.8M a year.

4 sites · 520 employees

• Took the lowest-ranked site to second in the network on productivity within 18 months.
• Built a supervisor development program; 7 of 10 site managers are now promoted from within.

• Ran a 600,000 sq ft site with 280 employees; held 99.6% order accuracy for 4 years.

GPA 3.7 / 4.0 · Dean’s List, 6 semesters

Relevant coursework: Regression Analysis, Database Systems, Machine Learning, Experimental Design

• Built a SQL pipeline and Tableau dashboard that tracks no-show rates across 14 clinics.
• Showed that reminder texts 48 hours ahead cut no-shows by about 9%; all 14 clinics adopted them.

• Cleaned and merged 3 survey datasets with 12,000 responses in R for a statistics faculty study.

• Automated a weekly inventory report in Python, saving the team about 4 hours a week.

Seasonal ARIMA forecast of a city’s daily bus ridership from 5 years of open data; 6% average error.

Managed a $6,000 budget and brought in 4 speakers from industry.