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About the team & roleShipping Analytics team drives eBay’s key shipping initiatives through data-driven insights and advanced analytics. We are a global, fast-paced team passionate about making shipping at eBay more reliable, accurate, affordable and seamless through the use of AI, innovation, and experimentation.
We’re looking for a highly skilled and self-driven individual contributor with a strong track record of analytical excellence, technical depth, and business impact. You’ll apply advanced analytics, AI, and data science techniques to build scalable data products, uncover insights, and influence strategic decisions across eBay’s shipping ecosystem.
In this role, you’ll harness data, experimentation, and AI to launch managed shipping solutions, optimize carrier routing, improve delivery performance, and increase seller adoption of eBay labels. You’ll translate complex logistics data into actionable strategies that enhance reliability, reduce cost, and grow label profitability - working closely with Product, Business, Finance, and Engineering teams to make shipping smarter, faster, and more efficient globally.
What You Will AccomplishSolve complex, real-world business problems using AI and advanced analytics to optimize eBay’s domestic and cross-border shipping experience.
Drive analytics to expand and scale eBay’s managed shipping and label platform globally, defining success metrics and measuring business impact.
Build predictive models and carrier rate simulations to optimize cost, speed, reliability, and sustainability across global carrier networks.
Analyze customer pain points to identify friction and propose data-informed product and business solutions that drive label adoption.
Design and evaluate A/B experiments to guide smart decisions on product features, pricing, and policy.
Develop AI-powered systems for real-time anomaly detection and operational performance monitoring.
Build data pipelines and dashboards to democratize insights and accelerate decision-making across teams.
Collaborate with Product, Engineering, and Finance partners to translate analytical findings into measurable business outcomes.
Bachelor’s degree in Engineering, Computer Science, Economics, Statistics, Mathematics, or a related quantitative field (Master’s degree or MBA preferred).
8+ years of experience in data science, analytics, or a related quantitative role.
Proven track record as a strong individual contributor, independently owning high-impact analytical initiatives end-to-end.
Expertise in SQL, Excel, and data visualization tools; proficiency in Python or R preferred.
Hands-on experience in product analytics, experimentation (A/B testing), and causal inference.
Demonstrated ability to apply data science and AI techniques to drive measurable business impact and solve optimization problems.
Excellent communication skills with the ability to translate complex analytical insights into clear, actionable recommendations for diverse stakeholders.
Working knowledge of modern AI tools such as Gemini, GPT, and large language models (LLMs) for data-driven automation and insights.
Ability to thrive in a fast-paced, cross-functional, and collaborative environment.
Nice to have: Prior experience with global logistics, shipping operations, or carrier network performance data.
How can we model friction across the seller label purchase funnel to identify behavioral and product-level barriers to adoption?
How can large language models and AI-driven simulations recommend carriers that balance cost, reliability, and SLA compliance?
Which seller cohorts show the highest incremental response to targeted interventions, and how can predictive systems personalize label adoption at scale?
How do category, item value, and delivery speed influence buyer sensitivity to shipping costs across global markets?
How can AI improve the accuracy of package weight and dimension predictions, and what is the downstream impact on cost estimation and carrier selection?
What early signals can AI-based anomaly detection uncover in carrier network performance, and how can they be used to mitigate operational risks proactively?
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