We are currently hiring for a mixed role that is 70/30% ML Ops/Risk Data Scientist. Join us, and you will contribute to building our decision and risk engine.

• Oversee and deploy ML pipelines, from development to production.
  
• Administer CI/CD pipelines, ensuring tests succeed and artifacts are properly stored.
  
• Monitor model performance metrics and set up alert systems for anomalies.
  
• Develop credit, fraud scoring and other predictive models.
  
• Engage with stakeholders to understand requirements and manage expectations.
  
• Document processes, and share knowledge and expertise.
  
• Manage project planning, execution, and progress tracking.
  

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
  
• Proficiency in Python, SQL, and database management.
  
• Experience with Docker and deploying applications on cloud platforms such as AWS.
  
• Strong experience with CI/CD pipelines, automated testing, and deployment.
  
• Ability to use effectively monitoring tools and establish responsive alert systems.
  
• Robust documentation skills to clearly record processes and optimizations.
  
• Familiarity with SageMaker and other AWS services would be a significant advantage.
  

Benefits

• Competitive compensation.
  
• An agile culture with a flat hierarchy, offering the opportunity to tackle complex, real-world challenges.
  
• A team comprised of top-tier professionals with experience in leading consultancies and banks.
  
• The chance to play a pivotal role in building a data-driven culture.