You will lead the Data Science function and own how our client turn petabytes of ecommerce data into production-grade intelligence. Your team builds the models, experimentation frameworks, and ML systems that power personalization, forecasting, pricing, search, and automated marketing across our platform. This role is both strategic and hands-on. You’ll set the research direction and ensure models ship reliably to millions of end shoppers.

Key Responsibilities

Data Science Vision & Strategy
Define the 2 to 3 year strategy for applied AI in commerce. Identify high ROI problems in merchandising, lifecycle, attribution, fraud, and supply chain where ML can create defensible product advantage.

Model Development & Research
Lead development of production models including recommender systems, LLMs for catalog and content, demand forecasting, LTV prediction, anomaly detection, and causal inference for incrementality. Balance quick wins with foundational research.

ML Platform Partnership
Partner with Data Engineering and ML Platform to shape the feature store, training pipelines, eval frameworks, and real-time serving infrastructure. Ensure data scientists can iterate fast without sacrificing reliability.

Experimentation & Measurement
Own the experimentation platform and measurement standards. Drive adoption of A/B testing, CUPED, geo-tests, and causal ML so every product decision is grounded in lift, not vanity metrics.

Team Leadership
Hire, mentor, and grow a world-class team of data scientists, applied scientists, and ML researchers. Create a culture of rigor, curiosity, and business impact. Set the bar for technical review and scientific excellence.

Product Integration
Embed with PM and engineering to launch AI features into customer-facing products. Translate ambiguous ecommerce problems into clear ML formulations. Define success metrics and hold the team accountable to them.

Customer & GTM Support
Work with sales and solutions on strategic accounts. Explain how our clients models work, design custom POCs, and help customers trust and adopt AI-driven products. Represent data science in exec briefings and analyst reviews.

AI Safety & Governance
Establish standards for model monitoring, bias testing, explainability, and PII handling. Ensure compliance with GDPR, CCPA, and customer security requirements when using customer data for training.

What You’ll Need to Succeed

• Experience: 13+ years in data science or applied ML, with 3+ years leading data science teams. You have shipped models at scale in B2B SaaS, ecommerce, marketplace, or adtech environments.
• Technical Depth: Expert in Python, SQL, and ML frameworks like PyTorch, TensorFlow, or JAX. Strong foundation in statistics, experimentation, and modern ML including deep learning, embeddings, and LLMs.
• Domain Expertise: Deep understanding of ecommerce data and use cases. You know how to model sparse events, cold start, seasonality, and messy catalog data. Experience with search, recsys, forecasting, or marketing science is a must.
• Production Mindset: You have taken models from notebook to 99.9% uptime services. Familiar with real-time inference, feature engineering at scale, and model monitoring.
• Leadership: Built and managed teams of 8+ data scientists. You coach PhD and non-PhD backgrounds effectively. Can present complex findings to CEOs and go deep on loss functions with engineers.
• Product Sense: Strong intuition for what makes an AI feature useful vs. gimmicky. You prioritize roadmap based on data quality, latency, and measurable business impact.

Bonus Points

• Experience with LLMs, RAG, fine tuning, or agentic systems in production for commerce or support use cases.
• Published work or patents in recsys, forecasting, or causal inference.
• Built or scaled a multi-tenant experimentation or ML platform.
• Experience with privacy-preserving ML, federated learning, or clean rooms.
• Prior zero to one experience in early stage AI startups.