Data Scientist

Il y a 1 jour

Brussels, Belgique Randstad Digital Temps partiel 500 € - 550 € Contrat

Your Role

  • Model Development and Deployment
  • Deploy machine learning models, specifically using AWS solutions.
  • Monitor and maintain the performance and scalability of deployed models in both cloud and on-premise environments.
  • Implement best practices for version control, model tracking, and model lifecycle management.

Infrastructure Management

  • Design and manage scalable, reliable, and secure cloud and on-premise infrastructure for machine learning projects.
  • Ensure seamless integration between various infrastructure components.

DevOps Integration

  • Implement and maintain CI/CD pipelines for machine learning projects.
  • Adopt sound Infrastructure as Code (IaC) principles to ensure consistency, repeatability, and enhanced data-driven workflows.

Collaboration and Communication

  • Work closely with data scientists, data engineers, and other stakeholders to understand project requirements and deliver optimal solutions.

Continuous Improvement

  • Stay up to date with the latest developments in machine learning, cloud technologies, DevOps, and MLOps practices.
  • Identify and implement improvements to existing workflows and systems, including FinOps optimizations.

Incident Response and Troubleshooting

  • Participate in incident response activities—specifically those related to data integrity and service availability—to assist teams with root cause analysis.
  • Help troubleshoot and promptly resolve performance or data quality issues.

Your Profile

  • Education: Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Science, or a related field.
  • Experience: Proven track record as a Data Scientist, Machine Learning Engineer, Data Engineer, or in a similar role, with experience in both business and technical analysis.
  • Architecture & Design: Strong understanding of system architecture with the ability to translate business requirements into scalable technical designs and solutions.
  • Data & Analytics: Experience performing data analysis using Python (via scripting and Jupyter Notebooks).
  • Frameworks & Tools: Knowledge of modern data engineering practices and frameworks (e.g., dbt, Dagster). Experience with Amazon SageMaker and other AWS services is a plus.
  • MLOps & Infrastructure: Solid understanding of MLOps principles (deployment, monitoring, lifecycle management) and hands-on experience with both cloud and on-premises infrastructure.
  • Programming Skills: Proficient in Python and its data science ecosystem; familiarity with additional languages such as R, Java, or C++ is a plus.
  • DevOps & Databases: Experienced with DevOps tools and practices, including CI/CD pipelines, containerization (Docker), orchestration platforms (Kubernetes, Amazon ECS), as well as strong SQL skills and familiarity with NoSQL databases.
  • Soft Skills: Excellent analytical and problem-solving skills, strong stakeholder management and communication capabilities, and the ability to work independently, prioritize effectively, and manage multiple tasks in a fast-paced environment.