Sr.Data Scientist/Machine Learning Engineer

Title: Sr.Data Scientist / Machine Learning Engineer

Location: Minnetonka, MN

Duration: Long Term CTH

Rate: on W2

Description:

• Design, develop, and maintain anomaly detection and pattern recognition systems across large-scale healthcare and operational datasets, using techniques such as clustering, classification, time-series analysis, change-point detection, and graph-based analytics.
• Develop reusable feature engineering, scoring, and analytical components that support multiple enterprise use cases rather than isolated point solutions.
• Apply natural language processing, large language models, and other machine-learning techniques to unstructured and semi-structured data to surface patterns, themes, and emerging signals.
• Design and contribute to production-grade machine learning pipelines, including automated data preparation, feature generation, training, validation, deployment, scoring, and monitoring.
• Develop and maintain CI/CD workflows for data science solutions, including source control, automated testing, model versioning, and rollback capabilities.
• Establish monitoring for production analytical systems — model performance, data quality, feature drift, model drift, and pipeline health.
• Partner with engineering and technology teams to integrate models and services with enterprise applications, APIs, and downstream business processes.
• Communicate analytical findings, model behavior, and limitations clearly to both technical and non-technical stakeholders.

Required Qualifications

• 10 plus years experience
• Strong professional experience in Data Science, Machine Learning, advanced analytics, statistical modeling, or a related discipline.
• Strong hands-on programming capability in Python.
• Strong SQL skills and experience working with large relational or analytical datasets.
• Strong foundation in statistics, machine learning, model evaluation, and experimental design.
• Experience developing real-world models using techniques such as classification, clustering, anomaly detection, predictive modeling, time-series analysis, or related approaches.
• Experience with data preparation, feature engineering, target construction, validation, and model performance evaluation.
• Experience developing reusable and maintainable analytical code rather than exclusively notebook-based or ad hoc analysis.
• Experience helping move machine-learning or advanced-analytics solutions into production.
• Understanding of model scoring, deployment, monitoring, data quality, model drift, and production lifecycle considerations.
• Ability to work effectively when requirements, data, or solution approaches are incomplete or evolving.
• Ability to communicate analytical methodology, findings, limitations, and business implications clearly.

Preferred Qualifications

• Healthcare, payer, claims, payment-integrity, provider, member, clinical, financial, or other regulated-data experience.
• Hands-on experience developing anomaly-detection or emerging-pattern systems.
• Experience with supervised, semi-supervised, and unsupervised machine-learning techniques.
• Experience with advanced modeling approaches such as gradient boosting, ensemble methods, deep learning, graph-based methods, sequence models, or representation learning.
• Experience with model explainability, calibration, threshold optimization, and false-positive reduction.
• Experience with Snowflake and Azure.
• Experience working within containerized Data Science environments.
• Familiarity with production ML and MLOps practices such as model registries, versioning, CI/CD, experiment tracking, monitoring, and lifecycle management.
• Experience integrating analytical models into APIs, applications, decision systems, or enterprise workflows.
• Experience working across Data Engineering, Software Engineering, MLOps, Platform, and Cloud teams.
• Experience applying NLP, embeddings, or GenAI where unstructured information must be converted into structured data or incorporated into a broader analytical solution.
• Experience mentoring other Data Scientists, helping establish modeling standards, or guiding analytical design decisions.