Jobs → Data & Analytics → Data Scientist

About the Role We are looking for a Data Scientist to join Medica's team. This role focuses on MLOps infrastructure and deployment enablement, with responsibilities including designing, implementing, and supporting reusable MLOps infrastructure, templates, and deployment patterns. Key Responsibilities • Design and implement reusable MLOps infrastructure, templates, and deployment patterns • Build and improve CI/CD workflows for automated testing, model packaging, artifact versioning, release approval, deployment, rollback, and promotion across environments • Establish repeatable training and retraining patterns, production-readiness standards, technical documentation, and operational runbooks • Design and maintain monitoring for model performance, data and feature quality, drift, pipeline health, latency, failures, usage, and other operational indicators • Implement actionable alerts and diagnostic capabilities • Troubleshoot production issues across models, application code, data, pipelines, and platform components • Create governed and scalable development and deployment patterns in partnership with Data Engineering, IT, Security, and platform teams • Develop reusable components, documentation, and self-service capabilities • Evaluate emerging tools, approaches, and automation opportunities Requirements • Bachelor’s degree in Data Science, Machine Learning, Statistics, Computer Science, Software Engineering, or a related field, or equivalent relevant professional experience • Five or more years of relevant technical experience, including at least two years deploying or supporting machine learning or advanced analytics solutions in production • Strong proficiency in Python and SQL • Experience with Git-based source control, collaborative code review, automated testing, continuous integration, and release-management practices • Experience building or supporting automated pipelines for data processing, model training, model deployment, batch scoring, retraining, or related production workflows • Experience implementing solutions on Azure, Snowflake, or comparable cloud and cloud-data platforms