Data Science/Machine Learning Builder | 10+ Years | Remote | USA

Qwalton Private Limited; United States; Hybrid; USD 120,000 - 160,000; Full time

Job summary

Qwalton Services Limited is seeking a Data Science / Machine Learning Builder to drive advanced analytics and production-grade AI solutions across claims, customer service, and technology workflows. The role focuses on scalable, secure ML systems in a healthcare context and requires strong technical ownership, with hybrid work in Minnetonka, Minnesota.

The candidate will design end-to-end AI solutions, integrate NLP and embeddings, and ensure robust explainability, monitoring, and auditability

Qualifications

• Data Science / ML Engineer with hands-on delivery mindset.
• Experience building production AI systems in a healthcare context.

Responsibilities

• Design, build, deploy, and operate production AI, ML systems focused on claims integrity, customer service, and technology workflows.
• Develop multimodal systems using structured data, NLP, embeddings, deep learning, and generative AI for explainable decisions.
• Create robust feature pipelines, model evaluation, and scoring architectures with monitoring and drift detection.
• Collaborate with MLOps, data engineering, and platform teams for CI/CD, security, and auditability.
• Provide operational support and continuous improvement for ML systems in production.
• Follow modern software practices: source control, testing, IaC, containers, deployment automation.
• Perform data discovery, curation, and pipeline issue resolution.
• Work with AI and business teams to deliver end-to-end solutions with measurable outcomes.

Skills

Data Science

ML Engineering

NLP

Deep Learning

Generative AI

MLOps

Tools

CI/CD

Containers

Infrastructure as Code

Observability

Job description

This Position is strictly for the job seekers who are staying in USA only others are not eligible

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