About this role
Description
As WWGS is committed to building an AI-native, agentic organization, the heart of SSG's evolution is to reinvent how we engage sellers with an AI-first mindset. Delivering that ambition at scale demands breakthrough innovation in our foundational capabilities, at ever-faster speed. To meet these priorities with the pace and focus they require, we are standing up a dedicated team, organized as a POD model - a lean, cross-functional team led by a single owner who holds end-to-end decisions and the business outcome, so we can move at the speed the business demands.
Our priority is to build the capability to reach the right sellers at scale, anchored on two pillars: Seller Tagging and Good Contact. Through Seller Tagging, we will turn today's fragmented, siloed seller signals into a unified seller intelligence capability that deepens and broadens how well we truly understand every seller. Through Good Contact, we will resolve the seller identity gaps across our channels and systems, so that every engagement and every system that depends on it, can consistently reach the right seller contact.
The Data Scientist will be part of the POD team and support the team to establish structured data governance and model training framework, and trail predict models with real business use cases.
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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