Machine Learning Jobs in St. Louis, MO

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.

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.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Performs imagery and geospatial intelligence analysis supporting NGA and machine learning training data. Creates, reviews, verifies, annotates, and categorizes imagery and multi-INT data; applies photointerpretation, targeting, precision mensuration, GIS, and order-of-battle expertise. Trains and mentors junior analysts while collaborating across teams. Uses imagery exploitation and GIS software, geodatabases, and potentially Python scripting to produce intelligence products and support AI platforms.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Designs end-to-end architectures for digital products and platforms, translating business requirements into scalable technical solutions. Responsibilities include creating architecture artifacts and process maps, defining API and enterprise integration strategies, evaluating technologies, supporting AI and analytics initiatives, and guiding implementation teams. The role partners with product and engineering stakeholders while addressing scalability, security, governance, reliability, and operational supportability.

Designs, builds, and scales enterprise AI applications using classical machine learning, generative AI, RAG, and agentic architectures. Develops intelligent workflows, autonomous agents, knowledge systems, chatbots, copilots, and decision-support solutions integrated with enterprise platforms and APIs. Leads AI governance, evaluation, observability, security, and human-in-the-loop controls while advising clients on enterprise AI transformation. Requires extensive experience with Claude, OpenAI, LLMs, cloud platforms, vector databases, semantic search, and production-grade AI implementations.

Leads technical solution architecture for NGA and intelligence-community programs. Translates mission needs into cloud, hybrid, DevSecOps, modernization, integration, transition, and sustainment architectures. Develops CONOPS, roadmaps, data flows, technical narratives, proposals, demonstrations, and executive briefings. Integrates GEOINT, AI/ML, and computer vision in classified environments while supporting cybersecurity, RMF, ATO, operations, incident response, and system lifecycle management.

Supports payer modernization and health services consulting engagements by analyzing operational challenges, optimizing processes, applying business analytics, engaging stakeholders, implementing quality standards, and guiding transformation initiatives. Develops operational support frameworks, conducts market research, manages change, and advises clients on improving service delivery and business performance while mentoring junior team members.

Leads eDiscovery and data analysis workstreams for complex cyber incident response matters, overseeing data assessment, review, extraction, normalization, entity resolution, notification, reporting, budgets, vendors, quality, and delivery. Advises legal teams and clients on technical strategies, risks, workflows, and findings. Manages multidisciplinary teams, develops defensible methodologies, mentors staff, resolves escalations, and advances Cyber eDiscovery processes through analytics, automation, and emerging technologies.

Owns P&L performance, profitability, growth, and digital transformation for Small and Lower Mid-Market commercial insurance portfolios. Leads pricing, underwriting, portfolio monitoring, analytics, data visualization, predictive modeling, and machine learning initiatives. Partners with actuarial, product, engineering, technology, and distribution teams to implement process improvements and underwriting actions across digital channels and geographies. Communicates insights to stakeholders and senior leadership while leading cross-functional teams in an Agile operating model.

Leads geospatial research using remote sensing, GeoAI, machine learning, image processing, and multi-source satellite and drone data. Responsibilities include designing experiments, conducting field surveys, analyzing geospatial datasets, developing grant proposals, publishing findings, coordinating interdisciplinary projects, and guiding team members. Research areas include environmental monitoring, greenhouse gas emissions, precision agriculture, food and water security, and climate-related land-use studies.

Paid 9-week hybrid internship supporting data and business technology initiatives at the Federal Reserve Bank of St. Louis. Projects may involve Python and AWS database analysis, product ownership, security compliance, statistical analysis, policy research, data science, machine learning, and GenAI experimentation. Interns participate in networking, mentoring, professional development, and mission-driven projects.

Develop and deploy machine learning models and responsible AI solutions for clients. Evaluate and mitigate fairness, bias, transparency, explainability, robustness, privacy, and other AI risks. Manage the full AI development lifecycle, optimize existing systems, document methodologies, contribute to responsible AI standards and training, and support client proposals. The role requires collaboration, technical communication, and potentially extensive travel.

Supports FP&A and enterprise analytics by developing scalable Power BI reporting, financial models, dashboards, and automated processes. Analyzes financial and operational datasets to identify trends, risks, and opportunities; improves forecasting, budgeting, and recurring reporting workflows using Excel, Python, SQL, Power Automate, and Microsoft platforms. Collaborates with finance, data engineering, IT, and business stakeholders, trains users, maintains governance standards, and contributes to the Pigment planning platform. Evaluates AI-enabled tools to improve reporting efficiency, data quality, and decision-making.

Leads technical strategy and product adoption for major Retail and CPG enterprise accounts as Databricks’ primary technical voice. Partners with account executives, guides customers through data and AI transformations, builds technical champions, aligns solutions to business strategies, and mentors team members. Requires extensive pre-sales, big data, cloud, data science, programming, and production data systems experience.

Lead presales for data and AI initiatives: design and present Microsoft Fabric-based data platforms, run discovery and AI workshops, scope proposals, translate technical designs into business value, mentor delivery, and support go-to-market and thought leadership.

Design, build, validate, and deploy predictive models using Python and SQL. Apply causal inference, A/B testing, matching models, and AutoML to evaluate interventions. Partner with product and engineering to deliver scalable analytics, ensure data quality and model performance, and communicate insights to stakeholders.