Head of Data Science; E-commerce

Position: Head of Data Science (E-commerce)

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.

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.

2. Customer Insights & Personalization

• Deliver multi-objective personalization across web/app surfaces.
• Build recommenders, search relevance, semantic search, and LTR models.

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.

4. Forecasting & Inventory Optimization

• Build multi-layer forecasting models for buying and replenishment.
• Develop availability, stockout, returns/refund, and supply‑chain efficiency models.

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.

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.

7. Catalog Quality & Trust

• Apply CV/NLP for enrichment, duplication, attribute extraction, and size mapping.
• Build fraud/abuse detection with explainability and review layers.

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.

Technical Skills

• Strong Python/R/SQL and deep expertise in ML, DL, NLP, forecasting.
• Experience with Tensor Flow/PyTorch, Spark/Hadoop, and cloud platforms (AWS/GCP/Azure).
• Solid grounding in experimentation, causal inference, and statistical modeling.

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!

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