Sr. Data Scientist

Role - Sr. Data Scientist

Location: Minnetonka, MN (Onsite/Hybrid)

• Must be local to the Minnetonka, MN area and available for onsite work from Day 1.
• Healthcare Domain background Experience Must

Rate: $70/hr. on C2C/1099 (OR) $60/hr. on W2 (Max Rates)

Requirements:

• 5+ years of professional Data Science/ML experience.
• Bachelor's degree in Data Science, Statistics, Computer Science, Engineering, or related field.
• Strong Python/R/SAS and SQL skills.
• Experience with machine learning, anomaly detection, pattern recognition, predictive modeling, clustering, and time-series analysis.
• Healthcare experience with claims, clinical, member, provider, or operational data preferred.
• Experience with Azure, Snowflake, MLOps, CI/CD, and production ML pipelines preferred.
• Excellent communication and stakeholder management skills.

Preferred Qualifications:

• Master's degree in a quantitative, computational, or engineering discipline.
• Experience working with healthcare data, including claims, clinical, member, provider, financial, or operational datasets.
• Hands-on experience developing anomaly detection or pattern recognition systems using supervised, semi-supervised, or unsupervised learning techniques.
• Experience with advanced modeling approaches such as ensemble methods, deep learning, graph analytics, natural language processing, embeddings, or large language models.
• Experience with CI/CD platforms and automated deployment workflows for analytical or machine learning applications.
• Familiarity with MLOps practices including model registries, experiment tracking, automated testing, model versioning, deployment strategies, monitoring, observability, and model lifecycle management.
• Experience with workflow and pipeline orchestration technologies used to automate data processing, model training, scoring, and deployment.
• Experience integrating machine learning or analytical services with other technology systems through APIs, services, event-driven processes, databases, or enterprise applications.
• Experience working in cloud-based analytics environments, particularly Azure and Snowflake.
• Familiarity with containerization, infrastructure automation, or modern software engineering practices used to deploy and operate analytical workloads.
• Experience troubleshooting complex analytical systems across data, model, pipeline, infrastructure, and application layers.
• Ability to balance statistical rigor, technical scalability, explainability, maintainability, and business usability when designing analytical solutions.