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.