Machine Learning Jobs in St. Louis, MO

Leads procurement and strategic sourcing across client accounts and properties. Develops sourcing roadmaps, executes eRFPs, manages supplier relationships, audits, performance, contracts, governance, reporting, and cost-saving initiatives. Analyzes market and account data, applies AI and machine learning analytics, supports change management and training, and leads cross-functional improvement projects. The role requires client-facing stakeholder engagement, contract knowledge, analytical ability, and leadership experience in procurement, ideally within facilities-related categories.

Open general application for Rapsodo, a sports analytics company using computer vision and machine learning. Seeking driven team players passionate about sports and technology to contribute to state-of-the-art analytics solutions and growth across product teams. Applicants encouraged to apply to join a fast-paced, innovative company partnered with major sports organizations.

Lead design and build of a cloud-based big data analytics platform: implement analytics infrastructure, integrate streaming and batch data, develop distributed processing pipelines, manage predictive analytics models, and apply machine learning and Bayesian methods. Work involves Scala on OS X and AWS, Java and Clojure, Kafka/Samza, NoSQL, and following TDD, Agile, CI/CD practices.

Lead design and implementation of end-to-end data science architectures for multi‑INT geospatial intelligence. Build production Python pipelines, apply ML, network analytics, and advanced spatial techniques, create interactive visualizations and dashboards, mentor team members, liaise with stakeholders, and produce mission-ready intelligence products adhering to IC standards.

The Senior Technical Architect will lead AI SDLC transformations, create architectural solutions, mentor teams, and drive innovation in regulated industries.

Design, train, validate, and deploy ML models and production inference pipelines (CV, NLP, geospatial). Build MLOps workflows, scalable APIs, and AWS-deployed AI workloads. Monitor model drift, optimize performance, collaborate with data scientists, architects, and stakeholders, and document processes and integrations.