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.