Sponsor Kantor White collar

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

Amazon

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LokasiShanghai, CHN, Tiongkok
Model kerjaDi kantor
Diposting18 Agustus 2026

Tentang pekerjaan

Rangkuman Get Karier, bukan salinan iklan aslinya

Amazon cari senior data scientist buat program AI di tim New Business & New Seller, Shanghai. Yang dicari orang bergelar S2 di bidang kuantitatif dengan rekam jejak mengantar model machine learning dari perumusan masalah sampai pemantauan di produksi. Perlu juga pengalaman dengan bahasa statistik seperti R, SAS, atau Matlab.

Kualifikasi

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Gelar S2 di bidang kuantitatif seperti statistika, matematika, data science, business analytics, ekonomi, keuangan, teknik, atau ilmu komputer. Pengalaman memakai bahasa statistik seperti R, SAS, atau Matlab. Rekam jejak mengantar model machine learning secara utuh, dari perumusan masalah, rekayasa fitur, pelatihan, penerapan, sampai pemantauan.

Deskripsi & syarat asli

Teks asli dari perusahaan, apa adanya. Tidak kami terjemahkan atau ubah.

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

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