About this role
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.