Head of Data Science

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.

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.

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 TensorFlow/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 e commerce business's data science platform from the ground up, then APPLY NOW!