Data Scientist

Mayflower is a technology company that alters the entertainment industry to a new level of perception and engagement.

We are developing a partner advertising high-load system with a large RPS, convenient and modern interface. We are growing exponentially and successfully implementing new technologies to accelerate this growth and the system as a whole.

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