Data Scientist (MLOps & Production Engineering)

Location: Minnetonka, MN (Hybrid) – Local Candidates Only

Job Type: Contract

Rate: Up to $65/hr on C2C max

About the Role

We are seeking an experienced Data Scientist with a strong focus on MLOps and production-grade machine learning systems for our client. In this role, you will bridge the gap between data science and robust engineering, designing and supporting scalable MLOps infrastructure, deployment patterns, and monitoring frameworks across the complete ML lifecycle.

Key Responsibilities

< data-path-to-node="11">1. MLOps Infrastructure & 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 cross-environment promotion.

• Establish repeatable training/retraining patterns, production-readiness standards, technical documentation, and operational runbooks.

< data-path-to-node="13">2. Observability, Reliability & Model Operations

• Design and maintain monitoring for model performance, data/feature quality, drift, pipeline health, latency, failures, and usage.

• Implement actionable alerts, diagnostic capabilities, and robust root-cause analysis for production troubleshooting.

< data-path-to-node="15">3. Reusable Platform Patterns & Developer Experience

• Create governed and scalable development/deployment patterns in partnership with Data Engineering, IT, Security, and platform teams.

• Enhance reproducibility, dependency management, access controls, secrets management, and auditability.

• Develop self-service capabilities and reusable components to accelerate adoption by data science teams.

< data-path-to-node="17">4. MLOps Maturity & Continuous Improvement

• Evaluate emerging tools, automation opportunities, and build proofs of concept to improve deployment speed, reliability, and cost efficiency.

Required Qualifications & Technical Skills

• Education: Bachelor’s degree in Data Science, Machine Learning, Statistics, Computer Science, Software Engineering, or a related field (or equivalent professional experience).

• Experience: 5+ years of relevant technical experience, including at least 2+ years deploying or supporting machine learning/advanced analytics solutions in production.

• Core Languages: Strong proficiency in Python and SQL for writing production-quality code.

• DevOps & CI/CD: Experience with Git-based source control, collaborative code reviews, automated testing, continuous integration, and release-management practices.

• Pipelines: Demonstrated experience building automated data processing, model training, deployment, batch scoring, and retraining pipelines.

• Cloud & Data Platforms: Hands-on experience implementing solutions on Azure, Snowflake, or comparable cloud/cloud-data platforms.

• Engineering Best Practices: Solid understanding of software packaging, dependency management, containerization, logging, monitoring, and alerting.

• Collaboration: Excellent communication skills with the ability to bridge technical and business teams.

Preferred Qualifications

• Master’s degree in Data Science, Computer Science, Machine Learning, or related field.

• Direct experience implementing MLOps solutions within Azure and Snowflake ecosystems.

• Familiarity with model registries, experiment tracking, feature stores, and model-monitoring platforms.

• Experience with containerization, Infrastructure as Code (IaC), and workflow orchestration tools.

• Experience working in regulated environments or with healthcare datasets (claims, clinical, member, or provider data).