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.

You’ll thrive here if you have

• 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.

That can be a plus:

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

Conditions

• EU-based employment contract and a 3-year Cyprus work visa with full support for your relocation and visa processes, including assistance for your family.
• Full relocation package: flights to Limassol for you and your family, a company-covered apartment for the first month, and full relocation support to make your move smooth and hassle-free.
• Transparent performance reviews twice a year, with bonus opportunities and salary adjustments.
• Private medical insurance for you and your family, a corporate mobile plan (unlimited in Cyprus with roaming included), and interest-free support for car purchases.
• Provident fund (Cypus): a long-term savings plan co-funded by you and the Company together (available after probation) that grows throughout your time with us in Cyprus.
• Mindfulness & well-being support, including psychological assistance with 50% coverage.
• 50% coverage of school and kindergarten fees for your children.
• Fully covered sports benefits, and also access to in-house electric scooters and bike rentals, and cycling purchase compensation.
• Investment in your growth: paid language courses and access to suited-for-you development programs, including conferences, training programs, and coaching to support your professional journey.
• A culture of recognition: a peer reward program to celebrate your contributions.
• A fully equipped office in Limassol’s city center, with everything you need for deep work and collaboration.
• Free catering in the office and an in-house coffee bar with high-quality drinks and a health bar stocked with nutritious snacks.
• A strong engineering culture: international teams, corporate events, team buildings, and hackathons—because great work happens in great communities.

Recruitment process

• HR interview (40 min);
• Technical interview (1.5 hour);
• Final interview (45 min).