Logistics Analytics Specialist

As a Logistics Analytics Specialist, you will sit at the intersection of heavy data engineering, day-to-day e-commerce logistics operations, and data automation (including use of AI LLM and agentic models). You won't just build dashboards or run reports; you will own the end-to-end data pipelines that optimize our supply chain, from when the buyer searches for a product listed on the Shopee platform, to the moment the buyer receives the products.

Responsibilities

Logistics Insights & Performance Analytics

• Deep-dive into logistics data to track and optimize critical KPIs.
• Run ad-hoc analyses on delivery performance, bottle necks, and sudden spikes.
• Provide insights and recommendations to management based on data analysis.

Date Pipelines & Reporting

• Design, optimize, and maintain automated ETL (Extract, Transform, Load) data pipelines specifically for high-volume supply chain data
• Build, update, and manage BI dashboards to give operations managers real-time visibility on metrics and KPIs.

AI Integration & Automations Tasks

• Main PIC for Logistics team to test, refine, and roll out internal LLM models, and automation agents.
• Actively utilize generative AI tools and basic automation to speed up data cleaning, querying, and report generation as well as implementation of process automation for teams’ manual processes.
• Provide feedback to BI team to ensure the AI agents they build accurately understand e-commerce logistics nuances.

Requirements

• Bachelor’s degree in a quantitative field (e.g., Economics, Business, Statistics, Mathematics, Computer Science, Engineering) or equivalent practical experience.
• At least 2 years in analytics / BI / Business Analysis / Ops Analytics.
• Advanced Excel / Google Sheets skills.
• Strong SQL and data wrangling skills; ability to work independently with large datasets.
• Hands-on experience interacting with LLM APIs and basic knowledge of framework paradigms for internal agent architectures.
• A proactive, problem-solving mindset that thrives on ambiguity and rapid e-commerce campaign cycles.
• Solid analytical fundamentals: descriptive analysis, driver/root-cause analysis, cohort/funnel style thinking as applicable.
• Practical BI/reporting skills (e.g., dashboards, scorecards, automated reporting) and comfort maintaining existing assets.
• Exceptional communication skills—ability to translate dense technical metrics into simple, operational strategies.