Data Scientist Specialist

1 day ago

Athens, Attica, Greece beBeeMachine Full time
Job Description

We are seeking a highly skilled and experienced Machine Learning Engineer to join our team. As a key member of our data science group, you will be responsible for building, enhancing, deploying and monitoring data science solutions.

You will support data science aspects of the end-to-end media product development cycle by collaborating with machine learning engineers and data scientists. Your work will involve designing sophisticated algorithms, building robust data pipelines, and ensuring our models perform reliably at scale.

Required Skills and Qualifications
  • Develop and optimize statistical and machine learning models for regression and classification.
  • Build data preprocessing pipelines and feature engineering workflows.
  • Conduct model evaluation and validation using appropriate metrics and techniques.
  • Deploy models to production environments with guidance from senior team members.
  • Monitor model performance and data quality in production systems.
  • Troubleshoot and resolve issues with existing ML pipelines and services.
  • Optimize pipeline performance and reliability.
  • Contribute to webservices and APIs.
  • Use structured approach to integrating complex components into a modular framework.
  • Demonstrate strong interpersonal, communication and presentation skills.
Benefits
  • A bachelor's degree in Computer Science, Mathematics, Statistics or related quantitative field.
  • 5+ years of related professional experience.
  • Strong Python programming skills with experience in ML libraries (statsmodels, scikit-learn, XGboost, Pytorch ect.).
  • Experience in developing statistical and machine learning methodologies.
  • Hands-on experience with API development and web services.
  • Hands-on Experience with SQL databases and working with structured datasets (MySQL, PostgreSQL).
  • Proficiency in code versioning tools like Git.
  • Experience in explaining technical subjects to non-technical audiences.
  • Familiarity with Deployment strategies, Docker and basic DevOps and Infrastructure.
  • Familiarity with CI/CD.
  • Familiarity with big data tools (Spark, Hadoop, Hive) and distributed computing concepts.
  • Familiarity with MLOps practices and tools (MLflow, experiment tracking).
  • Knowledge of any cloud platform (Azure, GCP, AWS) using VMs and Storage will be considered a plus.

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