Jobs / United States / Ebay INC
SEO Data Scientist
Ebay INC · 🇺🇸 San Jose
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 478 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 314 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Ebay INC paid sponsored hires in similar roles21 certified filings for “Data Science - Analyst 5” (Data Scientists) in CA: $178k–$205k, median $190k. Most were filed at wage level II (73%) — 2 lottery entries, ≈31% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Ebay INC
The US Department of Labor certified 478 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 314 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Ebay INC →
About the role
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 and the role: Our team helps build the future strategic direction of eBay by defining where we play, how we win, and how we develop differentiated experiences for our customers. We focus on deeply understanding the needs of core buyers and translating those insights into strategies that strengthen eBay’s position in the market. To bring that strategy to life, we work in close coordination with Product, Technology, and regional business teams across the end-to-end customer experience. Within this space, we are seeking a Data Scientist for the SEO Analytics function at eBay. In this role, you will partner closely with the product and engineering teams, using eBay’s vast data ecosystem to uncover insights, influence the SEO roadmap, and drive customer-centric innovation across our marketplace. What you will accomplish: • Develop the SEO product roadmap by delivering insights and analytical thought leadership that influence design, technical, and organizational decisions. • Define and own success metrics, measurement frameworks, and experimentation roadmaps for key product areas, helping the team make faster and more confident decisions. • Understand and improve SEO/AEO performance in a constantly evolving environment. Identify root causes behind performance changes, including product launches, technical issues, algorithm updates, AI search trends, and external market factors. • Apply sophisticated statistical methods combined with predictive analytics to address complex, ambiguous product and marketplace problems with significant buyer and revenue impact. • Design and interpret A/B tests and other causal measurement approaches to evaluate product changes and find opportunities to improve the buyer experience. • Develop forward-looking analytical approaches, including predictive and experimental models, to better understand customer behavior and guide long-term product strategy. • Partner cross-functionally with Product, Engineering, and Business teams to translate complex findings into clear recommendations that improve buyer and seller outcomes at scale. What you will bring: • 8+ years of experience in analytics, data science, or a related quantitative field, ideally supporting product decisions in an e-commerce or consumer technology environment. • Strong command of A/B testing design, causal inference, and experimentation measurement. • Strong hands-on proficiency in SQL and one or more analytical programming languages such as Python or R, with experience turning large-scale data into actionable insights. • Experience applying statistical methods, experimentation, and machine learning approaches for solving real business problems, including descriptive and inferential analysis and predictive modeling. • Familiarity with modern machine learning frameworks and approaches such as ensemble models, deep learning, or transformer-based methods. • Proven ability to connect insights to product growth opportunities, think critically in ambiguous environments, and influence decisions through data-driven recommendations.