Senior Data Scientist, AI Program, CNGS NBS (New Business & New Seller) Amazon Business

About the role

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.

You will own the science pillar for Seller Intelligence within a cross-functional POD (PM + DE + DS + SDE), directly impacting seller engagement metrics across CN, IN, LATAM, and East-Asia expansion regions. Your models and data products will be consumed by 5+ downstream teams (ESM, NSR, MKT, NBS AI Ops, ROC) and influence $100M+ annual seller GMS through improved segmentation and contact optimization.

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.

Qualifications

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.