Top Data Science Jobs in St. Louis, MO

Design and train LLMs and agentic systems for cybersecurity using RLHF/RLAIF and supervised fine-tuning. Build agent architectures, tooling, retrieval/memory, and evaluation pipelines with rigorous statistical benchmarking. Optimize prompts and inference, collaborate with engineers to productionize prototypes, and drive research on agent planning, safety, and reliability.

This role involves applying advanced data science and machine learning methods to healthcare data, leading teams, managing client relations, and developing analytics products for payers, while mentoring junior staff.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

Build and deploy statistical and machine learning models for the Toast platform. Collaborate with product, engineering, and business teams to identify opportunities, develop data-driven solutions, evaluate models, and communicate insights to stakeholders. Work in a fast-paced environment and contribute to production ML systems and experimentation.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, demand forecasting, targeting, and personalization. Collaborate with engineers, product, and stakeholders to set architecture, metrics, and production best practices; mentor data scientists; lead experimentation and deploy/monitor models using cloud and MLOps tooling.

As a Data Scientist at SentiLink, you'll develop fraud detection models using machine learning, engage in product development, and support analysis across teams. This role emphasizes hands-on technical work and end-to-end project ownership in a fast-paced environment.

Design, refine, and operationalize geospatial analytic models to bring structure and defensibility to GEOINT. Extract, transform, and structure complex datasets using Python and SQL, integrate models with ArcGIS, automate workflows, build dashboards, and ensure GEOINT tradecraft integrity and transparency in scoring frameworks.

Support data science work by preparing and integrating structured and unstructured data, building datasets, conducting exploratory analysis, programming reports and dashboards, and assisting senior data scientists with machine learning model development, testing, and deployment using Python, R, SAS, and SQL.

Design, refine, and operationalize GEOINT analytic models to convert inconsistent geospatial attributes into defensible scoring frameworks. Automate workflows, build dashboards, integrate models with analytic platforms, and collaborate with analysts and leadership to ensure methodological rigor and transparency.

Design, refine, and operationalize geospatial analytic models to bring consistency and transparency to GEOINT analysis. Extract, transform, and structure complex datasets, automate workflows, build dashboards, integrate models with analytic platforms, and ensure tradecraft integrity and defensible scoring frameworks that withstand scrutiny.

Lead design and delivery of advanced analytics, AI/ML models, automation, and custom visualizations using Esri and Python tools to support Intelligence Community missions. Build pipelines, mentor team members, conduct peer reviews, translate customer requirements into technical solutions, and present findings to stakeholders.

Design, refine, and operationalize geospatial analytic models; structure GEOINT attributes into defensible scoring frameworks; automate workflows and build dashboards; integrate models with analytic platforms (ArcGIS); work with analysts and leadership to ensure tradecraft integrity, transparency, and repeatable mission-focused outputs using Python and SQL.

Design and implement statistical, ML, and geospatial models to extract actionable intelligence from large structured and unstructured datasets. Perform EDA, feature engineering, model evaluation, and operationalization on cloud-native secure platforms. Produce visualizations, dashboards, documentation, and collaborate with engineers, architects, and mission stakeholders to gather requirements and integrate analytical capabilities into operational systems.

Lead design and implementation of scalable data processes and analytics for geospatial and intelligence datasets. Develop Python-based mission-ready solutions and visualizations, manage multiple projects, provide senior-level technical direction, quality control, and collaborate across NGA and the Intelligence Community to produce integrated analytic products.

Lead design and evolution of large-scale nodal power market models. Define mathematical approaches, develop optimization/forecasting methodologies (MILP, SCUC/SCED), collaborate with engineers to productionize models, work with product and customers to translate market problems into software, mentor data scientists, and guide long-term product strategy.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Entefy seeks a Senior Data Scientist with a Master's or PhD in a relevant field, and 3+ years of experience. Responsibilities include delivering advanced analytics, leading teams, and extracting insights from datasets.

Develop analytic models and data processing workflows on structured and unstructured datasets; clean, transform, and enrich data; build visualizations and dashboards; integrate analytics into systems with development teams; collaborate with analysts and product owners to refine requirements; document methodologies and participate in Agile ceremonies to deliver within sprint commitments.

Design and maintain data models and pipelines for mortgage and lending platforms. Develop ML/AI, predictive and prescriptive analytics, and LLM/Generative AI solutions. Collaborate with engineering and business stakeholders to build scalable cloud and data warehouse solutions, ensure data quality and governance, and deliver dashboards and executive reporting.

Lead experimentation and causal inference at company-wide scale: define methodology, own statistical approaches for small samples and clustered data, apply causal methods when experiments arent feasible, run experiment review, build tooling and curriculum, and partner with analytics and AI/ML teams to ensure decision-grade results.

Lead design, build, and production deployment of generative AI products (LLM apps, agents, copilots, RAG/search). Rapidly prototype features, build APIs/services and retrieval pipelines, optimize performance, ensure responsible AI, and mentor team members while setting AI technical direction across the organization.

Lead quantitative analysis and modeling of large real-world health datasets to estimate population-level disease prevalence and incidence. Design and implement statistical and machine learning models, maintain data pipelines and dashboards, ensure data quality and reproducibility, collaborate with clinicians and public health experts, prepare scientific reports, and support team onboarding and training.

Lead Oracle-focused healthcare data quality and analytics: validate transactional data, build standardized KPIs and dashboards, develop predictive inventory and revenue cycle models, and collaborate with clinical and finance teams to ensure compliance and optimize operations.

Build lightweight, Slack- and Sheets-integrated AI agents to pull and analyze data from ad and web APIs, surface anomalies and trends, and produce alerts, reports, and LLM-generated content briefs and recommendations. Deliver scoped, fast iterations on multi-step pipelines including classifiers, scorers, and recommendation layers.