mylo

Data Scientist

• Multi-Domain Technical Strategy: Lead the development of ML solutions across diverse contexts, ensuring that models for Credit Risk, Pricing Elasticity, and Collection Optimization utilize shared infrastructure efficiently.
• MLOps Architecture: Champion the adoption of modern Model Serving frameworks and Feature Stores. Design workflows that ensure feature consistency between training and real-time inference.
• Engineering Standards: Establish rigorous standards for Data Versioning, experiment tracking, and Hyperparameter Optimization, ensuring all research is reproducible and production-ready.
• Production Deployment: Oversee the transition of models from notebook environments to low-latency production APIs. Ensure models are wrapped, containerized, and integrated seamlessly with backend services.
• Mentorship: Guide the team in best practices for Python software engineering, including testing strategies, code structure, and performance optimization.

Senior Data Scientist

Machine Learning Engineer

• Build and Maintain MLOps Pipelines: Design and implement robust CI/CD pipelines for training, evaluating, and deploying machine learning models.
• Manage Cloud Infrastructure: Provision, configure, and manage our cloud infrastructure (AWS, GCP, or Azure) using Infrastructure as Code (IaC) principles.
• Automate Everything: Automate the deployment, monitoring, and scaling of machine learning services and data pipelines.
• Containerization & Orchestration: Utilize Docker and Kubernetes to create and manage reproducible environments for our ML applications.
• Collaborate and Empower: Work closely with AI engineers and data scientists to understand their needs and create tools that streamline their workflows.
• Ensure System Health: Implement comprehensive monitoring and logging to ensure the high availability, performance, and security of our ML platform.

Senior Data Scientist

Senior MLOps Engineer