Team & Project Overview
The NBS Data Central team powers analytics, data science, and AI capabilities for Worldwide Global Selling (WWGS). We build scalable data products, and insight-generation systems that drive seller growth across 10+ marketplaces.
Seller Intelligence is a P0 foundation theme at the Global Selling level, formed by merging "One Tagging" and "Good Contact" workstreams. It provides seller identity, segmentation, and contact-reach infrastructure that underpins all downstream seller-facing AI workflows — including intelligent outreach, personalized recommendations, and automated engagement.
Scope of Impact
Own the science pillar for Seller Intelligence within a cross-functional POD (PM + DE + DS + SDE)
Directly impact seller engagement metrics across CN, IN, LATAM, and East-Asia expansion regions
Models and data products consumed by 5+ downstream teams (ESM, NSR, MKT, NBS AI Ops, ROC)
Influence $100M+ annual seller GMS through improved segmentation and contact optimization
Key job responsibilities
Design and deliver seller segmentation and propensity models at scale — incorporating GMS, category, growth trajectory, engagement signals, and lifecycle stage.
Build contact quality scoring and lifecycle management systems (coverage optimization, dormancy detection, reactivation modeling).
Define success metrics, experimentation frameworks (A/B, causal inference), and measurement methodology for seller engagement interventions.
Productionize ML models and data products — partner with engineering to deploy seller scores, contact quality indices, and recommendation signals.
Explore LLM/GenAI applications: automated insight generation from seller data, contact intent classification, and intelligent report synthesis.
Serve as the science representative in bi-weekly NBS theme reviews; present findings and proposals to theme Bar Raisers and leadership.
Collaborate with BIE team members to democratize analytical outputs via dashboards and self-serve tools.
Contribute to cross-marketplace seller behavior analysis supporting Global Expansion strategy (IN, KR, VN, LATAM).
Evaluate, integrate, and iterate on AI systems — assess new AI/ML tools, frameworks, and third-party models for applicability to seller intelligence use cases.
Basic qualifications
- 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
- Proven track record of end-to-end ML model delivery: problem formulation → feature engineering → training → deployment → monitoring
- Experience designing and analyzing A/B experiments at scale with rigorous statistical methodology
- Demonstrated ability to translate ambiguous business problems into well-scoped science deliverables
- Strong written and verbal communication — ability to present complex findings to non-technical stakeholders
- Experience working with or evaluating AI systems
Preferred qualifications
- Ph.D. in a quantitative field (Statistics, Machine Learning, Economics, Operations Research)
- Experience with NLP/LLM applications (text classification, intent detection, embedding-based retrieval, RAG pipelines)
- Experience in seller/customer segmentation, propensity modeling, or CRM/lifecycle analytics
- Proficiency with distributed computing frameworks (Spark, EMR, Redshift, Hive)
- Experience working in a marketplace or platform business (e-commerce, SaaS, fintech)
- Familiarity with causal inference methods (DID, RDD, synthetic control, instrumental variables)
- Experience mentoring junior data scientists or leading a small science team
- Track record of publishing research papers or creating reusable analytical frameworks
- Knowledge of knowledge graph construction, entity resolution, or identity systems
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