Senior Data Scientist – Search & Recommendations

• Analyze large-scale e-commerce search data to identify trends and improvement opportunities across search, ranking, recommendations, and personalization
• Investigate search behavior, including zero results, query reformulation, abandonment, position bias, CTR, and relevance versus business trade-offs
• Define and interpret search KPIs
• Rigorously evaluate online experiments across ranking, personalization, recommendations, and hybrid or vector search
• Build end-to-end metrics layers using event-level data and server-side search logs
• Transform raw tracking data into clear datasets and visualizations
• Lead analytical initiatives
• Collaborate with Product, Data Science, MLOps, and ML/Search Engineering teams to improve product and business outcomes

• Strong hands-on skills in SQL and Python, particularly Pandas
• Solid statistical knowledge
• Ability to create clear data visualizations
• Familiarity with the e-commerce search funnel
• 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 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
• Experience building multidimensional metrics layers and end-to-end analytical solutions at large e-commerce scale
• Ability to collaborate with Product Owners, Data Scientists, MLOps, and ML/Search Engineers
• Ability to lead senior-level initiatives and deliver measurable product and business impact
• Proactive and curious approach to complex problems
• 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
• Nice to have: Familiarity with NDCG, Recall@K, MRR, and MAP
• Nice to have: Knowledge of information retrieval, recommender systems, or ranking models

• Medical benefits
• Gym support
• Personalised fitness options
• Team events
• Healthy Habits Club
• Flexible work-life dynamic
• Mental wellbeing support
• Social wellbeing initiatives
• Hybrid-environment community and connection activities