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