Senior Data Scientist

eBay; Greater London; On-site; GBP 80,000 - 100,000; Full time

Job summary

A leading ecommerce platform is seeking a highly skilled individual to join their Shipping Analytics team in London. The role requires applying advanced analytics and AI to optimize shipping solutions and improve delivery performance. The ideal candidate will have over 8 years of experience in data science, strong SQL skills, and the ability to work effectively across teams. This position offers a unique opportunity to influence strategic decisions in a fast-paced environment.

Qualifications

• 8+ years of experience in data science or analytics.
• Proven track record as an individual contributor.
• Expertise in SQL, Excel, and data visualization tools.

Responsibilities

• Solve business problems using AI and advanced analytics.
• Build predictive models and carrier rate simulations.
• Analyze customer pain points to propose solutions.

Skills

Data analysis

SQL

Data visualization

Python

A/B testing

Communication

Education

Bachelor’s degree in Engineering, Computer Science, or related field

Master’s degree or MBA

Tools

Excel

Gemini

Large language models

Job description

At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts.

Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet.

Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.

About the team & role

Shipping 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 Accomplish

• Solve 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.

What You Will Bring

• 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.

Some Interesting Questions We’re Trying to Answer

• 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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