Data Scientist

Valuelabs; Minnetonka (MN); On-site; USD 120,000 - 155,000; Full time

Job summary

Valuelabs is seeking a senior ML engineer in Minnesota to design and deploy end-to-end ML solutions across the full lifecycle, collaborating with Data Science, Engineering, IT and business teams. You will develop production-grade pipelines, monitor performance, and communicate complex concepts clearly.

The role emphasizes cross-functional teamwork, robust documentation, and ownership of operational excellence in production environments with a focus on reliability and governance.

Qualifications

• Bachelor’s degree in Data Science/ML/CS/Software Engineering or related field, or equivalent experience.
• 5+ years of relevant technical experience, including at least 2 years deploying or supporting ML/analytics in production.
• Strong Python and SQL with production-quality code.
• Experience with Git-based source control, code review, automated testing, CI/CD, and release management.
• Experience building or supporting automated data and ML pipelines for training, deployment, scoring, retraining.
• Experience implementing solutions on Azure, Snowflake, or comparable cloud platforms.
• Knowledge of packaging, dependency management, environment configuration, artifact versioning, and reproducible development practices.
• Experience implementing logging, monitoring, alerting, and production troubleshooting.
• Strong software engineering and systems-thinking skills.
• Strong problem-solving and written/verbal communication skills.

Skills

Python

SQL

Git-based source control

CI/CD

Automated pipelines

Software engineering

Root-cause analysis

Communication

Cross-functional collaboration

Documentation

Problem solving

Education

Bachelor’s degree in Data Science/ML/Statistics/CS/Software Engineering or related field

Master’s degree in Data Science/CS/ML/Statistics/Software Engineering (preferred)

5+ years of relevant technical experience (including 2+ years in production ML)

Tools

Azure

Snowflake

Kubernetes

Terraform / IaC

Airflow / data pipelines tooling

Model monitoring platforms

Job description

• 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
• 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
• 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
• Commitment to continuous improvement, operational ownership, and knowledge sharing

Qualifications:

• Bachelor’s degree in Data Science/Machine Learning/Statistics/Computer Science/Software Engineering, or a related field, or equivalent relevant professional experience
• 5 or more years of relevant technical experience, including at least 2 years deploying or supporting machine learning or advanced analytics solutions in production

Required Skills:

• 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
• Strong software engineering and systems-thinking skills
• Strong problem-solving and root-cause-analysis capabilities
• Strong written and verbal communication skills

Preferred Skills:

• 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