Timothy
Juliano

Core Responsibilities:

• Evaluate recommendation engine accuracy by analyzing user repurchase data and testing deduplication features.
• Evaluate AI-generated product recommendations through side-by-side A/B testing.
• Measure relevance of product suggestions within "You May Also Like" panels on Product Display Pages.
• Measure user interests through behavioral analysis on early-stage datasets.

Initiatives & Impact:

• Automated Evaluation Tool: Standardized evaluation of repurchase rates, reducing AHT by 11%.
• Cross-Regional Automation Scaling: Deployed Excel-based automation across 7 countries, resulting in a 47.70% reduction in AHT and a 187.46% increase in handle efficiency.
• Behavioral Spreadsheet Solution: Built an automated tracking system, reducing error rates by 56.52% and AHT by 5.65%.

Core Responsibilities:

• Evaluate e-commerce promotional videos and product SKU lists through structured content audits to enforce compliance.
• Measure short video quality by cross-referencing hard performance metrics with subjective user engagement indicators.
• Measure livestreaming content quality by conducting targeted random sampling audits to isolate performance outliers and resolve bugs.

Core Responsibilities:

• Evaluate search autocomplete, query recommendations, and intent mapping to ensure high accuracy.
• Evaluate search suggestion taxonomy through rigorous quality assurance testing.
• Evaluate query-to-product-image alignment through cross-modal testing.
• Evaluate recommendation panel outputs by validating semantic relationships.
• Evaluate competitive search performance by benchmarking against major competitor platforms.
• Evaluate search safety and integrity against prohibited or unsupported content guidelines.
• Evaluate internal LLM infrastructures for factual consistency and reasoning.
• Evaluate AI-driven games (Bagelbell) and game localization (Plants on Fire) for bugs and logic.