Data Scientist

Job Responsibilities

• 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.
• Design and manage scalable, reliable, and secure cloud and on-premise infrastructure for machine learning projects.
• Ensure seamless integration between different infrastructure components.
• Implement and maintain CI/CD pipelines for machine learning projects.
• Adopt sound Infrastructure as Code (IaC) principles to ensure consistency and repeatability, enhancing data-driven workflows.
• Work closely with data scientists, data engineers, and other stakeholders to understand project requirements and deliver optimal solutions.
• Stay up-to-date with the latest developments in machine learning, cloud technologies, and DevOps and MLOps practices.
• Identify and implement improvements to existing workflows and systems, including FinOps.
• Participate in incident response activities, especially those related to data integrity and service availability, to help teams dig into root cause analysis.
• Help troubleshoot and resolve performance or data quality-related issues promptly.

Must Have Skills

• Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Science, or a related field.
• Proven experience as a Data Scientist, Machine Learning Engineer, Data Engineer, or in a similar role.
• Experience conducting both business and technical analysis.
• Strong understanding of system architecture, with the ability to translate business requirements into scalable technical designs and solutions.
• Experience performing data analysis using Python through scripting and Jupyter Notebooks.
• Strong understanding of MLOps principles, including model deployment, monitoring, and lifecycle management.
• Hands-on experience with both cloud-based and on-premises infrastructure.
• Proficiency in Python and its data science ecosystem.
• Experience with DevOps practices and tools, including:
• CI/CD pipelines.
• Containerization using Docker.
• Orchestration platforms such as Kubernetes or Amazon ECS.
• Strong knowledge of SQL and familiarity with NoSQL databases.
• Excellent analytical and problem-solving skills, with the ability to think critically and creatively.
• Strong communication and stakeholder-management skills, with the ability to collaborate effectively across technical and business teams.
• Ability to work independently, prioritize effectively, and manage multiple tasks in a fast-paced environment.
• Professional working proficiency in Dutch and English.

Nice to have

• Experience with Amazon SageMaker and other AWS services.
• Knowledge of modern data-engineering practices and frameworks, such as dbt and/or Dagster.
• Familiarity with additional programming languages, such as R, Java, or C++.
• Experience with Infrastructure as Code tools and practices.
• Knowledge of model tracking, model registries, automated retraining, and model-monitoring solutions.
• Experience with FinOps, cloud-cost optimization, and performance optimization for machine learning workloads.
• AWS MLOps practices commonly include model training, deployment, monitoring, versioning, and lifecycle management at scale.

What's great in the job? Great team of smart people, in a friendly and open culture Expand your knowledge of various business industriesCreate content that will help our users on a daily basis Real responsibilities and challenges in a fast evolving company