What You’ll Do

Analyze & Optimize eCommerce Product Data – Run deep SQL & Python analyses to find opportunities in taxonomy, ontology, and structured data for search & discovery improvements.
Leverage LLMs for Analytical Solutions – Use prompt engineering techniques to create AI-driven approaches for taxonomy validation, data enrichment, and classification.
Evaluate & Improve AI/ML Models – Develop systematic evaluation frameworks for product knowledge models, embeddings, and semantic search solutions.
Drive Insights & Strategy – Use data-driven storytelling to influence product and AI teams, helping shape decisions on catalog optimization, entity resolution, and knowledge graph development.
Integrate with AI/ML Teams – Work closely with data scientists and engineers to test and refine AI-based classification, search ranking, and recommendation models.
Prototype and Experiment – Move fast, test hypotheses, and build quick experiments to validate structured data strategies and AI-driven discovery improvements.

What We’re Looking For

4 Years experience in analytics, data science role
Strong Data & Analytics Skills – Proficiency in SQL & Python for data wrangling, analytics, and automation.
Product Analytics Attitude – Familiarity with eCommerce search, recommendations, and knowledge graph applications
Strong Communication – Ability to turn complex findings into actionable recommendations for Product, AI, and Engineering teams.
AI/LLM Experience – Hands-on experience with LLMs, prompt engineering, and retrieval-augmented generation (RAG) for AI-powered insights (Preferred)
Model Evaluation Know-How – Ability to define metrics and evaluation frameworks for assessing ML-driven taxonomy and classification models..
Startup DNA – High agency, thrive in fast-paced, iterative environments with deep cross-functional collaboration.