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
We know that great talent deserves great conditions, so here's what you can expect when joining us:
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).