Data Scientist (Moderation)

Hybrid
CompanyMayflower
LocationLimassol, Lemesos, Cyprus
CategoryData & AI
DepartmentML Engineering
Seniority-
WorkplaceHybrid
Posted2026-09-02
Viarecruitee

Description

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

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Machine Learning & Modeling

  • Develop and train machine learning models for prediction, classification.
  • Build and evaluate regression, classification, clustering, and time-series models.
  • Design feature engineering pipelines and data preprocessing workflows.
  • Evaluate model performance, robustness, and production readiness.
  • Deploy and maintain ML models in collaboration with ml-ops teams.

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Data Analysis & Exploration

  • Explore large datasets to identify patterns, trends, and hidden relationships.
  • Perform statistical analysis and hypothesis testing.
  • Distillate open-source datasets by proprietary data via LLM agents
  • Detect anomalies, outliers, and unexpected metric movements.
  • Translate business or product questions into analytical tasks.

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Data Pipelines & Infrastructure

  • Work with data pipelines and streaming data systems.
  • Process and transform large datasets using Python and SQL.
  • Work with event streams and messaging systems (Kafka).
  • Contribute to data quality monitoring and dataset validation.
  • Work with analytical databases and data warehouses.

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Model Monitoring & Experimentation

  • Design experiments and evaluate model performance in real environments.
  • Implement monitoring for model performance and data drift.
  • Support A/B testing and experimentation frameworks.
  • Improve models based on production feedback and metrics.

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Cross‑Functional Collaboration

  • Work closely with ML engineers, data engineers, and product teams.
  • Communicate model results and analytical insights clearly.
  • Contribute to the development of ML best practices within the team.