Data Scientist

Qualification:

• 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, including the ability to design, review, debug, test, and improve production-quality code.
• 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, with the ability to become productive across the technology environment.
• Understanding of software packaging, dependency management, environment configuration, artifact versioning, and reproducible development practices.
• Experience implementing or supporting logging, monitoring, alerting, and production troubleshooting capabilities.
• Ability to diagnose issues across model behavior, code, data, pipelines, infrastructure, permissions, and platform services.
• Experience writing clear technical documentation, deployment instructions, architecture guidance, and operational runbooks.
• Ability to communicate technical concepts clearly and collaborate effectively across Data Science, Engineering, IT, Security, platform, and business teams.

Preferred Qualifications

• Master’s degree in Data Science, Computer Science, Machine Learning, Statistics, Software Engineering, or a related discipline.
• Direct experience implementing MLOps solutions with Azure and Snowflake.
• Experience with model registries, experiment tracking, feature management, model-monitoring platforms, or managed machine learning services.
• Experience with containerization, infrastructure as code, workflow orchestration, cloud-native deployment, or platform-engineering practices.
• Experience implementing secure credential management, role-based access controls, auditability, and data-protection controls.
• Experience with model governance, validation, responsible AI practices, or regulated production environments.
• Experience working with healthcare data, including claims, clinical, member, provider, or operational datasets.

Core Skills and Abilities

• Strong software engineering and systems-thinking skills.
• Ability to design solutions across the complete machine learning lifecycle rather than optimizing a single component in isolation.
• Ability to balance delivery speed with reliability, security, maintainability, and governance.
• Strong problem-solving and root-cause-analysis capabilities.
• Ability to translate recurring technical needs into reusable tools, patterns, and documentation.
• Ability to work independently while coordinating effectively across multiple technical and business teams.
• Strong written and verbal communication skills.

Commitment to continuous improvement, operational ownership, and knowledge sharing