Top Remote Machine Learning Jobs in St. Louis, MO

Lead implementations of iQueue for Operating Rooms from discovery through configuration, validation, and rollout. Partner with hospital, clinical, operational, IT, and executive stakeholders to optimize OR workflows, scheduling, staffing, utilization, and access. Responsibilities include data analysis, solution design, product configuration, user acceptance testing, change management, risk monitoring, stakeholder alignment, and go-live readiness. Travel to customer sites is required up to 40–50%.

Designs, builds, scales, and operates distributed CI/CD services supporting millions of builds, tests, and releases. Develops code for Azure-based infrastructure spanning virtual machines, containers, and custom lab environments. Responsibilities include requirements analysis, architecture and design documentation, estimation, debugging, optimization, operational playbooks, on-call incident response, monitoring, reliability improvements, and mentoring engineers.

Provides technical leadership for architecture and evolution of large-scale distributed data platforms supporting Azure network telemetry. Designs streaming and batch systems, establishes reusable engineering patterns, drives cross-team technical strategy, and owns capabilities from design through production operations. Ensures scalability, reliability, security, performance, data quality, and operational excellence while mentoring engineers and influencing technical leaders. The role also involves architectural reviews, dependency management, risk reduction, and applying AI or machine learning approaches to operational systems.

Own technical wins for strategic enterprise sales, partnering with Account Executives to discover customer needs and develop production-ready solutions. Build proofs of concept, demonstrations, prototype AI agents, and supporting tools while engaging AI teams, data platform teams, and enterprise architects. Apply hands-on experience with LLMs, agents, data science, machine learning, or data engineering to explain how agents retrieve data and use business context.

Develop and deploy machine learning, statistical, operational research, and semantic analysis solutions for large, complex datasets. Build applied, predictive, and prescriptive analytics; conduct exploratory data analysis; assess and cleanse data; collaborate with software and data engineers to commercialize algorithms; and document and communicate findings to technical teams, stakeholders, and customers.

Lead technical client onboarding, migrations, solution design, integrations, data orchestration, troubleshooting, and project delivery for Movable Ink’s AI-powered personalization platform. Partner with clients and internal teams to implement custom workflows, APIs, webhooks, and data solutions. Conduct training and workshops, provide product expertise, advise on roadmap improvements, and support long-term client success across email and mobile marketing programs.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Serve as primary client contact for a portfolio of loyalty-program clients. Scope and manage end-to-end actuarial analyses rooted in predictive modeling and machine learning, validate model outputs, translate results into clear recommendations and presentations, and partner with analysts and data scientists to deliver high-quality, client-facing work in a fully remote consulting environment.

Leads cyber risk assurance strategy and multidisciplinary teams responsible for control monitoring, testing, evidence management, issue remediation, audit support, regulatory readiness, and assurance automation. The role advances continuous, AI-enabled assurance by translating regulatory and cybersecurity requirements into measurable outcomes, maintaining assurance frameworks, evaluating control effectiveness, managing concurrent activities, and reporting metrics to support executive risk decisions.

Analyze telematics, IoT, sensor, and firmware data to evaluate hardware performance, reliability, and field issues. Build dashboards and reports, identify trends and anomalies, apply statistical and machine learning techniques, validate firmware improvements, and provide actionable insights to engineering, product, data science, and customer support teams. Help develop scalable data pipelines and promote data-driven hardware and software development.

Own and negotiate complex commercial transactions, including customer agreements, research collaborations, partnerships, data licenses, SaaS contracts, vendor agreements, and professional services agreements. Partner with sales, science, finance, and R&D teams, particularly on life sciences and computational chemistry deals. Build scalable legal infrastructure through playbooks, templates, fallback positions, self-service processes, and AI-enabled contract workflows.

Leads go-to-market strategy and product marketing for emerging AI products in the defense market. Responsibilities include positioning, messaging, launch planning, technical content, demonstrations, market research, competitive analysis, product evangelism, and cross-functional collaboration with Product, Engineering, Growth, and executive teams. The role translates complex technical capabilities into customer value and supports product adoption in new or undefined markets.

Leads technical product marketing strategy for UDS Fleet, including positioning, messaging, go-to-market planning, launches, adoption, market research, competitive analysis, technical content, demonstrations, and sales enablement. Partners with Product, Engineering, Growth, and leadership to communicate the value of edge, distributed, and secure software deployment capabilities across defense environments. Serves as a product evangelist and translates complex technical concepts into customer-focused narratives.

Leads Lexia’s business analytics and data strategy function, managing analysts and overseeing data engineering alignment. Owns BI strategy, Tableau governance, data governance, self-service dashboards, AI-enabled analytics, metric definitions, and data quality. Partners with Sales, Marketing, Customer Success, executives, and analytics engineering to translate business needs into insights, KPIs, models, and actionable recommendations. Manages analytics tools and vendors while ensuring responsible, secure, and effective use of enterprise data.

Leads technical enablement and sales success for strategic national channel partners. Aligns partner and company go-to-market strategies, develops business plans, conducts technical and sales training, supports evaluations and configurations, and serves as a trusted technical advisor throughout the sales cycle. Builds executive relationships, coordinates training and marketing activities, gathers partner requirements, mentors technical managers, and collaborates with sales, systems engineering, and channel teams.

The Director of Data owns the company’s data platform, governed metrics, client identity resolution, data quality, privacy controls, and AI-ready datasets. This leader manages the data team, establishes governance and operating standards, oversees warehouse and pipeline architecture, and ensures reliable data for Finance, leadership, products, and ML/LLM systems. The role requires hands-on technical leadership, roadmap ownership, stakeholder communication, and accountability for data reliability, lineage, security, and platform costs.

Leads strategy, roadmaps, backlogs, discovery, delivery, and continuous improvement for AI-powered products. Partners with engineering, AI/ML, data, design, and business teams to translate customer needs into measurable solutions using generative AI, LLMs, agents, and automation. Manages stakeholders, product metrics, responsible AI considerations, and complex consulting engagements, including PMO and multi-team delivery responsibilities.

Leads the architecture, design, and implementation of enterprise AI solutions. Defines AI strategies, scalable architectures, technology roadmaps, governance practices, and modern technology stacks. Partners with business and technical stakeholders, guides development teams, evaluates emerging technologies, supports consulting pursuits, and provides technical leadership throughout project lifecycles. The role also mentors architects and engineers and requires client relationship development and travel.

Leads enterprise master data and product data architecture, defining target-state designs, data models, governance, quality, enrichment, classification, matching, and AI-enabled automation. Translates complex business and technical needs into scalable, secure solutions; evaluates platforms and vendors; manages architecture risks and trade-offs; and mentors architects, engineers, analysts, product owners, and delivery teams. Partners with senior leaders to advance Ferguson’s master data, product data, and responsible AI strategy.

Lead the evolution of trading infrastructure by managing trade operations, P&L attribution, risk monitoring, data integrity, and performance reporting. Develop Python- and machine-learning-based AI agents, MLOps workflows, anomaly detection, predictive risk models, automated reconciliations, and real-time dashboards to transform manual trading operations and improve risk analysis.

Leads clinical intelligence initiatives by sizing AI opportunities, conducting data discovery, evaluating AI/ML solution performance, and measuring impact, accuracy, drift, and patient outcomes. Partners with product, clinical, engineering, compliance, and operations teams to develop measurement plans, value hypotheses, benchmarks, and scalable intelligence capabilities. The role is remote with occasional travel and requires advanced SQL, Python, clinical analytics, and population health research expertise.

Develop and deploy AI-driven applications, workflows, integrations, and ServiceNow solutions for enterprise customers. Gather requirements, configure and script ServiceNow modules, implement Now Assist and other AI capabilities, integrate external systems through REST, SOAP, MID Servers, and ETL, and support testing, documentation, deployment, incident remediation, service mapping, and event management. Collaborate with stakeholders and technical leads while maintaining consistent platform patterns.

Owns core backend computation services that produce correct, reproducible, traceable, and auditable financial results. Designs configuration-driven architectures, data-intensive services, and set-based warehouse computations. Requires strong Python, SQL, data modeling, system design, reliability, isolation, and testing expertise. Provides technical leadership through design reviews, engineering standards, and mentorship while partnering with data, AI, and product teams.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Oversee global analytics and algorithm design initiatives for a nonprofit: identify high-impact projects, coordinate with student directors, collect feedback, design and run training for developers and technical advisors, and form partnerships to support implementation. Volunteer commitment ~5-6 hours/week.