Data Scientist в команду оценки благосостояния

About the role

About the team

Responsibilities

• 6+ years of engineering experience, including 3+ years building production ML systems (NLP, LLM applications, embeddings, or classification at scale)
• 2+ years as an Engineering Manager or ML Team Lead at a fast-growing e-commerce, marketplace or fintech company
• Hands-on experience shipping LLM-based products: prompt and pipeline design, fine-tuning, evaluation, cost and latency control, self-hosted and API-based models
• Experience building and operating large-scale data and ML pipelines (batch and streaming), and making them observable, reproducible and reliable
• Solid backend fundamentals; you are comfortable reviewing Go and Python services and reasoning about distributed systems
• Our stack: Python, Go, PostgreSQL, Pub/Sub, BigQuery, GCS, Kubernetes, Google Cloud Platform, Airflow, and a microservices architecture
• A strong grasp of ML evaluation: golden datasets, labeling workflows, offline metrics, and A/B testing tied to business outcomes
• Product sense: you connect catalogue quality to conversion, discovery and merchant growth, and you can prioritise accordingly
• A proactive mindset and the ability to work independently
• Strong communication skills in English (B2 level or higher)
  
  

• Experience with product catalogues, PIM systems, or marketplace content moderation
• Experience with Arabic-language content
• Familiarity with data residency and regulated-data requirements

Qualifications

• Own the end-to-end product data pipeline: ingestion from feeds and plugins, ML enrichment, moderation and publication, with clear SLAs for freshness, coverage and quality
• Lead the ML roadmap for catalogue intelligence: category tree and attribute coverage, translation quality, ML-assisted moderation, item embeddings and recommendations
• Lead large cross-team projects and drive them to production
• Contribute to quarterly planning and roadmap definition; define and report OKRs for catalogue quality and personalisation
• Review feature designs and ensure non-functional requirements are met, including ML evaluation, inference cost, latency and data residency
• Build and maintain the evaluation and labeling infrastructure that lets the team measure every model change before it reaches production
• Oversee technical debt management and incident handling across ML and backend services
• Hire, evaluate, and motivate team members; grow ML engineers into owners of business outcomes
• Build cross-team and cross-functional collaboration with Shopping, Offers, Monetisation, catalogue operations and partner support to increase efficiency
• Foster a results- and business-oriented culture
• Monitor key team performance indicators
• Ensure process and delivery transparency for stakeholders and partner functions
• Optimise processes to improve productivity

Benefits

• Full-time B2B contract
• Fully remote setup
• Up to 20% tax allowance
• 22 paid leave days annually
• Stock options (ESOP) in a fast-scaling, pre-IPO company
• Flexi benefits you can use for wellness, travel, or learning
• Work alongside a high-performing, international engineering team in a global fintech unicorn

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).