Data Science/Machine Learning Builder

Need & for this role
Job Description:

• Client seeks a highly skilled Data Science / Machine Learning Builder to drive advanced analytics, anomaly detection, predictive modeling, and production-grade AI solutions across Claims and Payment Integrity, Customer Service, and Technology workflows.
• This role emphasizes hands-on delivery of scalable, secure, and maintainable ML systems in a healthcare context, with a focus on multimodal approaches, deep learning, and integration into enterprise workflows.
• The ideal candidate will possess strong technical ownership, production mindset, and the ability to translate complex business problems into robust analytical solutions.
• The position is based in Minnetonka, Minnesota, with hybrid work expectations and a preference for local talent.

Roles and Responsibilities:

• Data Science / Machine Learning Builder, Production AI Systems Developer, Design, build, deploy, and operate production AI, machine learning, and analytical systems with a focus on claims integrity, customer service, and technology workflows.
• Develop and integrate multimodal systems combining structured data, NLP, embeddings, deep learning, and generative AI for enhanced decision-making and output explainability.
• Engineer robust feature pipelines, population/target definitions, model evaluation frameworks, and scoring architectures with strong emphasis on explainability, monitoring, and drift detection.
• Collaborate with MLOps, data engineering, and platform teams to ensure CI/CD, observability, security, compliance, and auditability of deployed models and services.
• Own operational support for ML systems, including troubleshooting, root-cause analysis, and continuous improvement in production environments.
• Apply modern software engineering practices including source control, automated testing, infrastructure as code, containers, and deployment automation.
• Conduct data discovery, curation, and integration work, including diagnosing and resolving pipeline issues where necessary.
• Work closely with AI/Automation and business teams to design end-to-end solutions that deliver measurable operational and business outcomes.
• Demonstrate autonomy in ambiguous environments, make sound technical tradeoffs, and deliver high-velocity, reviewable work.