Senior Data Scientist – Search & Recommendations

Responsibilities  

Search Analytics & Insights → Analyze large-scale e-commerce search data to identify trends and improvement opportunities across search, ranking, recommendations, and personalization

Query & Ranking Analysis → Investigate search behavior, including zero results, query reformulation, abandonment, position bias, CTR, and relevance versus business trade-offs

KPI Measurement & Experimentation → Define and interpret search KPIs and rigorously evaluate online experiments across ranking, personalization, recommendations, and hybrid or vector search

Data & Metrics Development → Build end-to-end metrics layers using event-level data and server-side search logs, from raw tracking data to clear datasets and visualizations

Cross-Functional Analytical Leadership → Lead analytical initiatives and collaborate with Product, Data Science, MLOps, and ML/Search Engineering teams to improve product and business outcomes

Must Have

• Strong hands-on skills in SQL and Python, particularly Pandas, supported by solid statistical knowledge and the ability to create clear data visualizations
• Familiarity with the e-commerce search funnel and hands-on experience with query and ranking analysis, including zero results, reformulation, abandonment, position bias, CTR by position, and relevance versus business trade-offs
• Experience defining, tracking, and interpreting metrics such as CTR, PDP view rate, add-to-cart, conversion, revenue, and zero-results rate
• Experience designing and statistically evaluating ML/search experiments across ranking, personalization, and recommendations
• Proficiency with event-level data and server-side search logs, including building multidimensional metrics layers and end-to-end analytical solutions at large e-commerce scale
• Proven ability to collaborate with Product Owners, Data Scientists, MLOps, and ML/Search Engineers, lead senior-level initiatives, and deliver measurable product and business impact
• Proactive and curious approach to complex problems, with genuine enthusiasm for search, recommendations, ranking, personalization, and AI-powered product discovery
• Regular use of AI tools to improve productivity, automate or reduce repetitive work, support decision-making, and deliver higher-quality outcomes
• Ability to use AI tools responsibly by structuring effective prompts, critically validating outputs, understanding limitations, and taking ownership of the final result

Nice to Have

• Experience with GCP, distributed data systems, and analytical databases
• Familiarity with offline relevance evaluation metrics: NDCG, Recall@K, MRR and MAP
• Knowledge of information retrieval, recommender systems, or ranking models

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