Sr. Data Science/Machine Learning Builder _ Minnetonka, MN (Day1 Onsite) _ 12+ Years Experience

Requirement details:

Job Title: Sr. Data Science/Machine Learning Builder

Location: Minnetonka, MN (Day1 Onsite)

Duration: 12+ months

Job Description:

12+ Years of Experience

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.