Data Scientist (Search & Recommendations)

Mayflower is a technology company building high-load products used by millions of people worldwide. Operating at the scale of one of the world's top-50 websites, it solves complex engineering challenges and creates solutions that power real-time entertainment for a global audience.

• 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
• Analyze failed searches, irrelevant results, zero-result queries, and other search-quality issues
• Explore lexical, semantic, behavioural, hybrid, and vector search approaches
• Build, train, and optimize 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
• Optimize models for low-latency inference and investigate relevance degradation, bias, and feedback loops
• 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
• 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 analyze 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
• 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

• Strong hands-on experience building production search, ranking, or recommendation systems
• Strong Python and SQL skills for machine learning, data processing, and analytical queries
• Practical experience with learning-to-rank, candidate retrieval, search relevance, or recommender-system modelling
• Experience building and evaluating multi-stage retrieval and ranking pipelines
• Strong understanding of search and recommendation metrics, including Precision, Recall, NDCG, MAP, MRR, CTR, and conversion
• Experience with feature engineering and behavioural data such as impressions, clicks, sessions, and conversions
• Experience with ML libraries such as scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, or TensorFlow
• Experience working with large-scale production systems, distributed data processing, analytical databases, and streaming platforms
• Strong understanding of experimentation and A/B testing, with the ability to independently build and validate ML solutions

Будет плюсом:

• Experience with Elasticsearch, OpenSearch, Solr, Lucene, or another search-engine stack; vector databases, approximate nearest neighbour search, and hybrid lexical-semantic retrieval; query understanding, classification, spell correction, synonyms, or query expansion; DSSM, two-tower models, BERT-based ranking, cross-encoders, or similar neural architectures; large-scale data and ML platforms such as Airflow and MLflow

• EU-based employment contract and a 3-year Cyprus work visa, with full support for relocation and visa processes, including assistance for family
• Full relocation package: flights to Limassol for the candidate and family, a company-covered apartment for the first month, and relocation support
• Transparent performance reviews twice a year, with bonus opportunities and salary adjustments
• Private medical insurance for the candidate and family, a corporate mobile plan with unlimited use in Cyprus and roaming, and interest-free support for car purchases
• Provident fund in Cyprus, co-funded by the employee and the Company and available after probation
• Mindfulness and well-being support, including psychological assistance with 50% coverage
• 50% Coverage of school and kindergarten fees for children
• Fully covered sports benefits, access to in-house electric scooters and bike rentals, and cycling purchase compensation
• Paid language courses and access to development programs, conferences, training programs, and coaching
• Peer reward program
• Fully equipped office in Limassol’s city center
• Free office catering, an in-house coffee bar, and a health bar with nutritious snacks
• International teams, corporate events, team buildings, and hackathons
• Recruitment process: HR interview (40 min), technical interview (1.5 hour), and final interview (45 min)