Pricing Analyst & Commercial Intelligence Specialist

Reconciled gross-to-net revenue across three brands and more than 600 SKUs from WooCommerce API and ERP systems. Applied deduplication and timestamp controls to achieve parity with the source of record, creating a trusted dataset for revenue and margin analysis and saving approximately four hours per week.

Automated daily WooCommerce API ingestion using Google Apps Script for Data Studio dashboards, enabling daily SKU-level tracking against sales targets. Built channel-level price and margin scenarios supported by Python time-series elasticity models; the resulting dashboards informed pricing and Hot Sale/Cyber catalog decisions, contributing to 26% real revenue growth in Hot Sale 2026 versus 2025.

Designed controlled A/B tests using RFM segmentation for more than 20,000 customers, improving retail-segment repurchase by 7 percentage points versus the control group. Built a daily campaign reporting pipeline with Python, Pandas, NumPy, PyMuPDF, and Excel under RAP methodology, eliminating 15 hours of manual work per week.

Reviewed operational documentation to identify errors, inconsistencies, and areas requiring clarification.

Developed process manuals and training materials to support standardized operations and improve documentation quality.

Mapped five critical business processes and assessed failure points, redundancies, and operational control gaps.

Documented redesigned procedures and controls in the form of SOPs adopted by management. The process redesign reduced redundancies by 30%.

Designed a Python ETL process to extract, clean, and validate web data for more than 1,000 organizations.

Consolidated the validated information into a centralized Excel database, reducing data-collection time by 60% and improving the accessibility of research data.