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Mayflower

Data Scientist (Moderation)

Limassol, Lemesos, Cyprus · Remote · fulltime_permanent · ML Engineering

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About the role

Machine LearningA/B TestingPythonSQLKafkaLLMsStatisticsData Analysis
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 - 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. - 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. - 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. - 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. - 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.