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.