Hiring for Data Scientist // HYBRID - C2C

Job Title: Data Scientist - C2C

Location: Minnetonka, MN;

Duration: Long term Contract

Mode of Work: HYBRID

I am also including the Job Description below:

Job Description:

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 Medica 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.

Key Accountability

Key Responsibilities

MLOps Infrastructure and Deployment Enablement

Design, implement, and support 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.

Observability, Reliability, and Model Operations

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; conduct root-cause analysis and implement durable corrective actions.

Reusable Platform Patterns and Developer Experience

Create governed and scalable development and deployment patterns in partnership with Data Engineering, IT, Security, and platform teams. Improve reproducibility, testing, dependency management, access controls, secrets management, auditability, and maintainability. Develop reusable components, documentation, and self-service capabilities that make approved patterns easier for data science teams to adopt.

MLOps Maturity and Continuous Improvement

Evaluate emerging tools, approaches, and automation opportunities. Develop proofs of concept and reusable assets that improve deployment speed, reliability, governance, cost efficiency, and overall MLOps maturity. Recommend practical improvements based on measurable organizational needs.