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We’re looking for a hands-on Data Science leader with product/e-commerce experience and startup-build exposure—someone who can set strategy from scratch while remaining an IC, not a pure people leader or program director. This is a perfect role for someone ready to step up into leadership, hire a small team, and scale a practice to 5–10 people.

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You will lead the intelligence layer of our marketplace, powering personalization, search/ranking, pricing, demand forecasting, inventory optimization, marketing science, and real-time agentic AI. You’ll partner with Product, Engineering, Marketing, Supply Chain, and Commercial to turn data into measurable business outcomes.

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What You Will Lead

1. Strategy & Leadership

• Own and execute the data science strategy tied to growth KPIs (acquisition, activation, AOV, repeat rate, margin).

• Build a roadmap balancing fast iteration with long-term foundations (feature store, real-time inference, experimentation).

• Hire and develop a multi-disciplinary DS/ML/MLOps team.

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2. Customer Insights & Personalization

• Deliver multi-objective personalization across web/app surfaces.

• Build recommenders, search relevance, semantic search, and LTR models.

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3. Pricing & Merchandising Science

• Lead dynamic pricing, elasticity models, and competitive price-matching.

• Optimize promotions, assortment, and attribute coverage.

• Apply causal inference for pricing/promo impact.

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4. Forecasting & Inventory Optimization

• Build multi-layer forecasting models for buying and replenishment.

• Develop availability, stockout, returns/refund, and supply-chain efficiency models.

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5. Marketing Science & Experimentation

• Own full-funnel attribution, incrementality, and ROAS optimization.

• Lead always-on experimentation with rigorous guardrails.

• Deliver LTV, CAC, churn, and audience segmentation models.

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6. Agentic AI & Automation

• Build real-time agentic systems for merchandising, pricing, and operations.

• Implement human-in-the-loop workflows and feedback loops for continuous learning.

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7. Catalog Quality & Trust

• Apply CV/NLP for enrichment, duplication, attribute extraction, and size mapping.

• Build fraud/abuse detection with explainability and review layers.

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8. Data Platform, MLOps & Governance

• Collaborate with Engineering to scale the lakehouse, feature store, and streaming ecosystem.

• Implement mature MLOps (CI/CD, registries, canary/shadow deployments, monitoring).

• Champion governance, privacy, and model risk practices.

Qualifications

• Master’s or PhD in a quantitative field.

• 12–15+ years applied DS experience (marketplace/e-com preferred).

• Demonstrated success shipping production ML that moved KPIs at scale.

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Technical Skills

• Strong Python/R/SQL and deep expertise in ML, DL, NLP, forecasting.

• Experience with TensorFlow/PyTorch, Spark/Hadoop, and cloud platforms (AWS/GCP/Azure).

• Solid grounding in experimentation, causal inference, and statistical modeling.

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If you fit the brief of the role and have built a product or ecommerce business's data science platform from the ground up, then APPLY NOW!