Senior Data Scientist - Global E-commerce, Supply Chain & Logistics - Global E-Commerce Supply Chain and Logistics

Global E-commerce is a discovery-driven e-commerce business, working to become a destination for users to discover and buy good products at affordable prices. We build a shopping experience that feels tailored to each user, and a platform merchants can rely on across live and short-video commerce. The Supply Chain and Logistics team is a key pillar of that business, covering selection of goods, procurement, warehousing, and inventory management. We improve the customer experience through warehousing, supply chain, and logistics technology, giving merchants dependable fulfillment so they can simplify and expand their business worldwide. This means integrating digital systems with physical operations across warehouses, customs, transportation, and distribution networks, at high concurrency and with little margin for error at any step between seller and buyer. You will build the systems that move real goods across borders, where a software decision shows up in a warehouse, a customs queue, and a delivery window. Responsibilities: 1. Lead the adoption of state-of-the-art AI agent technologies across the data science workflow. Design and productionize agentic solutions for data discovery, analysis, insight generation, decision support, and automated monitoring, with clear evaluation standards, guardrails, and human oversight. 2. Build intelligent insight and decision capabilities that proactively surface business and technical issues, recommend actions, and accelerate the cycle from opportunity identification through experimentation, iteration, and impact measurement. 3. Develop scalable analytics and modeling solutions for use cases, including demand and capacity forecasting, fulfillment and transportation optimization, merchant performance, network planning, anomaly detection, and simulation. 4. Use experimentation, causal inference, and rigorous measurement frameworks to evaluate logistics strategies, product changes, and operational programs; clearly distinguish correlation from causal impact. 5. Partner with Product, Engineering, Operations, and business stakeholders to define high-value problems, establish success metrics, translate analyses into product or operational decisions, and drive solutions through implementation. 6. Create reusable data products, analytical frameworks, and self-service tools that improve the speed, consistency, and quality of decision-making across the organization. 7. Raise the technical bar for applied data science by establishing best practices in reproducibility, model and agent evaluation, monitoring, documentation, and responsible AI use. Minimum Qualifications: - Bachelor’s degree or higher in Data Science, Computer Science, Statistics, Mathematics, Operations Research, Economics, Engineering, or a related quantitative field. - 5+ years of industry experience in data science, applied machine learning, quantitative analytics, operations research, or a related field. - Hands-on experience applying large language models or AI agents to real analytical or operational workflows, including tool use, orchestration, evaluation, monitoring, and failure handling. - Strong proficiency in SQL and Python or R, with experience working with large, complex datasets and building reproducible analytical or modeling pipelines. - Solid foundation in statistical modeling, experimentation, causal inference, machine learning, forecasting, optimization, or simulation. - Demonstrated ability to convert ambiguous business questions into structured analytical approaches and measurable recommendations. - Strong written and verbal communication skills, with experience influencing cross-functional partners and explaining technical concepts to non-technical audiences. Preferred Qualifications: - Master/Doctoral degree in a quantitative field. - Experience in e-commerce, supply chain, logistics, marketplace, fulfillment, transportation, or network optimization. - Experience deploying AI- or ML-powered products and decision systems in production, including offline and online evaluation, observability, safety controls, and human-in-the-loop workflows. - Familiarity with modern data and distributed-computing technologies such as Spark, Hive, Flink, cloud data platforms, and workflow orchestration tools. - Experience with algorithm engineering, data products, or optimization systems that operate at scale. - Curiosity about emerging AI technologies and a record of translating new methods into reliable business applications.