Data Scientist (Search & Recommendations)

Mayflower is a technology company building highload products used by millions of people worldwide. Operating at the scale of one of the world's top-50 websites, we solve complex engineering challenges and create solutions that power real-time entertainment for a global audience. Now we look for a Data Scientist to join our ML team Job Responsibilities - Search & Retrieval - Develop and improve retrieval pipelines for large-scale production search systems. - Work on candidate generation, query processing, matching, filtering, and retrieval strategies. - Improve search relevance, result coverage, and overall SERP quality. - Analyse failed searches, irrelevant results, zero-result queries, and other search-quality issues. - Explore lexical, semantic, behavioural, hybrid, and vector search approaches. - Ranking & Relevance - Build, train, and optimise ranking models for search and recommendation systems. - Develop learning-to-rank solutions using behavioural, content-based, contextual, and real-time features. - Design ranking features based on clicks, conversions, popularity, freshness, availability, and user behaviour. - Evaluate ranking quality using Precision, Recall, NDCG, MAP, MRR, and related relevance metrics. - Optimise models for low-latency inference and investigate relevance degradation, bias, and feedback loops. - Recommendation Systems - Develop recommendation models and candidate-generation strategies for personalised and non-personalised scenarios. - Build recall and ranking stages for multi-stage recommendation pipelines. - Work on related-item, complementary-item, next-action, and behavioural recommendation use cases. - Develop user, item, session, and contextual representations. - Balance relevance, diversity, novelty, coverage, and business constraints. - Experimentation & Evaluation - Design and run offline and online experiments for search, ranking, and recommendation improvements. - Build evaluation frameworks that connect model quality with product and business outcomes. - Design and analyse A/B tests using CTR, conversion, engagement, retention, and revenue-related metrics. - Create reproducible pipelines for data preparation, model training, evaluation, and comparison. - Evaluate model robustness across traffic segments, query groups, user cohorts, and edge cases. - ML Pipelines & Collaboration - Build end-to-end ML pipelines for feature generation, training, validation, deployment, and monitoring. - Work with high-load, real-time, and low-latency production systems. - Process large datasets using Python, SQL, batch pipelines, streaming systems, and Kafka. - Collaborate with product, backend, data engineering, and MLOps teams to productionise ML solutions. - Communicate technical decisions, experiment results, and trade-offs while contributing to ML best practices.