Top Senior Machine Learning Jobs in St. Louis, MO

Leads enterprise product evaluations, implementations, integrations, and adoption initiatives. Manages cross-functional projects involving AI, automation, system modernization, and third-party platforms. Translates business requirements into implementation plans, develops business cases and ROI analyses, coordinates vendors and stakeholders, prepares executive communications, assesses risks, and supports organizational change and user adoption.

Owns the intelligence platform roadmap for enterprise healthcare engagement, including decisioning, orchestration, integrations, interoperability, identity and consent management, and scalable deployment. The role defines strategy and requirements, prioritizes platform investments, leads partner integrations, establishes success metrics, and coordinates Engineering, AI/ML, Data Science, Security, Design, and client teams. It also embeds healthcare compliance, security, and responsible AI throughout the product lifecycle.

Provides strategic finance leadership to venture-backed AI and SaaS startups. Responsibilities include monthly close oversight, budgeting and forecasting, capital planning, treasury management, board reporting, investor updates, fundraising, M&A support, cap table analysis, revenue cycle management, cash flow planning, controls, audit readiness, and advising founders on financial strategy.

Build edge-focused full-stack product experiences integrating machine learning and autonomy for frontline defense users. Collaborate with designers, infrastructure teams, product teams, and ML engineers to create intuitive applications, efficient edge-deployed application stacks, and internal modeling APIs. Develop maintainable software using strong engineering practices, including design documents, code reviews, and style guides.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Leads enterprise product and technology integrations, cross-functional projects, AI and automation initiatives, business analysis, vendor coordination, executive communications, and change adoption. Develops requirements, implementation plans, business cases, risk strategies, and executive reporting while ensuring initiatives meet scope, schedule, compliance, and organizational objectives.

Lead the data science function, owning machine learning strategy, predictive models, healthcare analytics, DBT data marts, and executive dashboards. Analyze claims, eligibility, pharmacy, EMR, HIE, clinical, and operational data to identify high-risk patients, evaluate clinical interventions, and improve patient and provider engagement using AI and LLMs. Collaborate with clinical, operations, and technology teams while leading data science staff in a fast-growing healthcare organization.

Manage client relationships throughout implementation and ongoing support, serving as the primary client contact. Oversee KPIs, operational metrics, strategic planning, project execution, and client goal attainment. Translate client needs into platform opportunities, represent customer feedback to Product and Engineering, and develop case studies and success materials. The role requires healthcare client success or account management experience, strong communication, project leadership, and knowledge of medication management and healthcare quality measures.

Own customer-facing AI and data engagements from discovery through production launch. Define business value, MVPs, success criteria, and product requirements; prioritize engineering backlogs; lead technical discovery, architecture reviews, agile delivery, testing, production readiness, launch, adoption, and strategic handoff. Build C-suite relationships, translate business needs into technical requirements, resolve delivery risks, and identify future high-value opportunities.

Design and implement automated testing frameworks for data platforms, pipelines, distributed processing systems, and machine learning workflows. Develop strategies for data quality, schema integrity, drift, model accuracy, bias, regression, performance, and resilience. Investigate production failures, conduct exploratory testing, embed continuous validation into delivery processes, collaborate across engineering and data teams, and mentor junior SDETs.

Conduct computational discovery and optimization of rare-earth-lean and rare-earth-free permanent magnets. Execute DFT and multiscale simulations, develop machine-learning workflows and interatomic potentials, generate high-throughput materials data, and connect atomistic predictions to microstructure and manufacturing processes. Collaborate with experimental teams to validate models, contribute to publications, patents, project planning, and technical presentations, and support junior researchers.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

Lead Amazon performance marketing strategy and execution, manage Sponsored Ads and DSP, collaborate with agencies and cross-functional teams, optimize campaigns and budgets, analyze performance with AMC/analytics, and measure commercial impact to drive ROAS and customer acquisition.

Lead CX data intelligence initiatives using SQL, dashboards, analytical models, and AI/LLM techniques. Analyze customer journeys, operational metrics, churn risks, and retention indicators; develop insight agents and self-service reporting; define CX health metrics; and translate findings into strategic recommendations, process improvements, and experiments. Partner with operations, support, product, data, and engineering teams while maintaining data quality, governance, and documentation.

Own and execute multi-year strategic partnerships with a hyperscaler and an AI lab, aligning technical integrations, commercial motions, and joint go-to-market plans. Coordinate cross-functional teams, run operational cadences, track measurable outcomes (pipeline, ARR, integrations), and represent partnerships to executives while driving hands-on execution and follow-through.

Build and maintain Safari AI’s cloud infrastructure, including production services, system design, reliability, documentation, and security compliance. The role sets technical direction for the cloud stack, collaborates with engineering and operations teams, improves development processes, and delivers incremental features. Candidates should have 4–6 years of software engineering experience, public cloud expertise, and experience taking complex services from concept through production. Streaming processing and machine learning experience are advantageous.

Design, develop, implement, support, and improve financial-system integrations, databases, reporting solutions, and automated processes. Lead technical financial systems projects from requirements gathering through implementation, focusing on Aderant Expert, SQL Server, SSIS, SSRS, and T-SQL. Collaborate with Finance, IS, vendors, and business stakeholders; troubleshoot complex data issues, optimize database performance, document technical solutions, and support multiple concurrent initiatives.

Own Litify’s AI Call Intelligence product line end to end, including call capture, transcription, summaries, conversational insights, voice agents, and agentic workflows. Define the roadmap, quality standards, guardrails, and evaluation practices; lead client discovery, beta programs, competitive research, backlog prioritization, and cross-functional delivery. Partner with engineering, design, data science, and go-to-market teams to launch products, drive adoption, and measure outcomes.

Leads Sophos’s Americas MSP channel strategy, revenue growth, partner productivity, and operating performance. Oversees recruitment, onboarding, enablement, retention, distribution relationships, recurring-revenue forecasting, strategic negotiations, and program optimization. Builds and coaches direct and matrixed teams while influencing product, pricing, investments, and partner-program decisions. Uses performance data and market intelligence to improve activation, adoption, retention, expansion, and return on investment across cybersecurity managed-service offerings.

Leads complex AI client projects from requirements gathering through delivery, serving as the primary client contact and coordinating Sales, Operations, Product, and Engineering. Responsibilities include defining scopes and delivery plans, managing risks, quality, timelines, budgets, profitability, and KPIs, resolving execution challenges, and improving scalable processes for AI project delivery.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Develops, calibrates, validates, and maintains credit risk models, including PD, LGD, and EAD models, for lending, collections, loss forecasting, capital planning, and stress testing. Analyzes large datasets, evaluates model performance, documents model changes, supports systems testing, and communicates statistical insights to stakeholders and management. Collaborates across the credit union to improve risk-return strategies and reporting.

Leads complex enterprise SaaS delivery programs for major banks, owning governance, client and executive relationships, commercial controls, risk management, regulatory deadlines, third-party coordination, and successful go-live. The role requires deep financial crime compliance technology experience, including AML, KYC, transaction monitoring, sanctions screening, or case management platforms, plus proven Tier 1 bank implementations, program turnaround expertise, cloud delivery, and senior stakeholder credibility.

Lead independent validation and ongoing monitoring of complex financial and analytics models, including ALM, loss forecasting, loan transition, corporate finance, and engagement models. Identify weaknesses, communicate findings, guide remediation, and strengthen model governance. Collaborate with Finance, Quantitative Research, Growth Analytics, Internal Audit, Internal Controls, Accounting, and Compliance to maintain Affirm’s model risk framework and address audit, regulatory, and examiner requests.