Top Machine Learning Jobs in Minneapolis, MN

Leads AI strategy and solution-delivery engagements for pharmaceutical, biotechnology, and medical technology clients. Advises senior executives, designs AI transformation roadmaps and scalable architectures, oversees multidisciplinary teams through implementation and value realization, and embeds responsible AI, privacy, cybersecurity, regulatory, and GxP considerations. The role also develops repeatable offerings, supports proposals and account growth, mentors senior leaders, and requires travel to client locations.

Serves as a client-facing data science and technology consultant for a healthcare data platform. Analyzes large-scale claims, clinical, and unstructured datasets; develops statistical, machine learning, predictive, and prescriptive models; and creates prototypes and scalable analytics solutions. Partners with product, engineering, and client services teams, translates client needs into recommendations, supports onboarding and troubleshooting, and provides technical guidance on AI, cloud analytics, and platform adoption.

Designs scalable enterprise architecture solutions across cloud and modern application environments. Translates business objectives into technical designs, diagrams, and architecture patterns; develops AI-powered solutions; evaluates technologies; builds proofs of concept; and guides engineering teams on governance, security, reliability, and maintainability. The role also mentors developers, improves workflows through automation, and collaborates with technical and non-technical stakeholders.

Designs and builds scalable, secure AI/ML solutions for healthcare clients, including LLM workflows, AI agents, copilots, and intelligent automation. Leads solution architecture, prototyping, cloud deployment, evaluation testing, troubleshooting, and production delivery. Translates ambiguous business and clinical needs into actionable technical solutions while advising stakeholders and collaborating across product and engineering teams.

Leads Optum Financial’s enterprise platform strategy and technology transformation across API and integration, data, AI-assisted development, quality engineering, and agile delivery. Owns platform adoption, engineering productivity, reliability, security, compliance, budgets, and cost optimization. Drives migration to a product operating model, develops global leadership teams, and aligns independent business domains around shared platforms and engineering practices in regulated financial and healthcare environments.

Designs, builds, deploys, and scales agentic AI solutions for healthcare clients. Responsibilities include architecting systems integrating LLMs, machine learning models, APIs, and enterprise platforms; developing production-grade AI applications; evaluating agent performance; troubleshooting cloud-based distributed systems; and translating business and clinical needs into actionable technical solutions. The role also leads client engagements, collaborates cross-functionally, and communicates recommendations to technical and executive stakeholders.

Leads AI strategy and solution-delivery engagements for life sciences clients. Manages workstreams, projects, stakeholders, risks, deliverables, and cross-functional teams from discovery through implementation. Applies pharmaceutical, biotechnology, and medical technology expertise to translate business and technical requirements into AI-enabled solutions. Coaches team members, reviews client deliverables, communicates findings to executives, supports business development, and contributes to consulting methodologies and reusable assets.

Leads strategic analytics and healthcare market intelligence initiatives supporting growth, market development, physician alignment, and service line planning. Evaluates healthcare opportunities using complex internal and external datasets, including utilization, referral, claims, demographic, and competitive data. Develops business cases, forecasts, executive presentations, and recommendations while managing analytics portfolios and coordinating with strategy, data engineering, platform, governance, and senior leadership teams.

Develop AI-enabled software solutions for internal digital transformation initiatives. Design, code, test, debug, and support scalable applications using modern engineering practices. Apply machine learning tools, automate workflows, analyze data, contribute to APIs, microservices, and cloud-native architecture, and translate business requirements into technical designs. Review junior team members’ work, provide technical guidance, and collaborate with internal stakeholders to deliver reliable enterprise applications.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Design, develop, test, deploy, and support enterprise applications using Java, Spring Boot, MuleSoft, microservices, Talend, and Azure. Build integrations, APIs, ETL and data conversion solutions, AI-enabled workflows, monitoring dashboards, and automation tools. Troubleshoot complex production issues in a 24x7 environment, conduct root-cause analysis, support claims and provider operations, and collaborate with QA, DevOps, infrastructure, and business teams.

Leads complex AI strategy and solution-delivery engagements for pharmaceutical, biotechnology, and medical technology clients. Serves as the primary client advisor, translating scientific, operational, technical, and regulatory needs into scalable solution designs and implementation plans. Manages governance, workstreams, risks, resources, and executive reporting while guiding multidisciplinary teams. Develops reusable AI delivery methods, reference architectures, playbooks, and enterprise offerings, incorporating privacy, cybersecurity, model risk, validation, GxP, and responsible AI considerations.

Leads Oracle Cloud Finance transformation workstreams, client engagements, and delivery teams across ERP and EPM programs. Designs finance processes, translates business requirements into Oracle Financials solutions, oversees configuration and deployment planning, and applies automation, analytics, and machine learning to improve finance operations. Manages risks, timelines, deliverables, and stakeholder relationships while coaching team members and identifying opportunities to improve budgeting, forecasting, reporting, and financial close processes.

Leads financial statement audits and evaluates governance, risk management, regulatory compliance, and internal controls for digital asset and financial services clients. Directs complex audit engagements, applies advanced auditing and technical accounting expertise, identifies material misstatements, and issues auditor reports. The role also involves client relationship management, coaching and developing diverse teams, communicating findings, and maintaining professional, independence, and ethical standards.

Designs and optimizes Snowflake data pipelines for clinical and claims data supporting HEDIS processing, quality analytics, chart abstraction, and care gap identification. Integrates AI, machine learning, and NLP models into healthcare data workflows; implements data quality, governance, security, and HIPAA-compliant practices. Partners with clinical quality and reporting teams to translate HEDIS specifications into automated solutions, while mentoring engineers and advancing MLOps and DataOps practices.

Leads discovery, architecture, prototyping, and delivery of generative AI and agentic solutions across finance, actuarial, healthcare economics, analytics, and operations. Translates business needs into requirements and technical designs, evaluates technology tradeoffs, develops reusable AI patterns, supports responsible AI controls, and coaches stakeholders. Partners with business and technical teams while guiding cross-functional delivery and remaining hands-on with code, low-code, analytics, and AI platforms.

Leads AI consulting engagements for payer organizations, translating business challenges into prioritized use cases, solution designs, implementation roadmaps, and measurable value cases. Manages client relationships, multidisciplinary teams, project economics, scope, schedules, staffing, and risk. Partners with sales on proposals and statements of work while developing reusable offerings. Integrates privacy, security, regulatory compliance, model risk, and responsible AI principles into healthcare solution delivery.

Direct enterprise data science strategy and lead production machine learning, Generative AI, agentic AI, and advanced analytics initiatives. Establish modeling, validation, responsible AI, monitoring, and lifecycle standards while architecting scalable ML and LLM systems. Mentor senior data scientists, influence cross-functional delivery, and translate healthcare payment integrity, claims, coding, fraud, waste, and abuse challenges into AI solutions. Requires extensive healthcare data, production ML, healthcare financial integrity, GenAI, and agentic AI experience.

Leads multiple software engineering teams modernizing large-scale medical transaction batch systems. Drives AI adoption and AI-driven development practices for migration, refactoring, testing, and architecture. Oversees cloud platforms, microservices, CI/CD, security, performance, and operational reliability while partnering with product, architecture, and executive stakeholders. Develops engineering talent, establishes delivery roadmaps, and promotes responsible AI, data governance, and healthcare security compliance.

Lead the modernization of Chewy’s fulfillment technology and warehouse management systems. Define architecture, write production code, build resilient cloud and edge systems, develop mobile warehouse applications, establish workflow and integration patterns, and integrate analytics, AI, robotics, and decisioning systems. Provide technical direction across teams, lead design and code reviews, improve engineering practices, and mentor engineers while ensuring high availability, observability, auditability, and operational continuity.

Leads the design and modernization of scalable data architecture for economic data platforms, including massive time-series datasets, archival documents, and research publications. Responsibilities include data modeling, metadata and governance design, storage and processing architecture, indexing, discoverability, security, lifecycle management, and API data access optimization. Partners with application architecture and cross-functional teams to deliver highly available, secure, public-facing data platforms supporting analytics and research.

Develop and deploy AI capabilities across enterprise ERP platforms including Workday and SAP Ariba. Build intelligent agents, chatbots, automation, integrations, and custom GenAI/LLM solutions. Establish PaaS environments, cloud services, deployment pipelines, security, governance, and engineering processes. Lead AI projects from concept through production, evaluate vendor versus custom solutions, support technology roadmaps, and promote AI literacy. Provide production support and training while aligning solutions with business needs and measurable ROI.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.