Senior Data Scientist – Search & Recommendations
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Job Description
Senior Data Scientist analyzing e-commerce search, ranking, and recommendation performance at Accesa. Building metrics and evaluating experiments to improve AI-powered product discovery.
Responsibilities:
- 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
Requirements:
- 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
Benefits:
- 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
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