Top Data Science Jobs in St. Louis, MO

The role involves preparing traffic data, developing and deploying machine learning models on Google Vertex AI, and collaborating with teams to improve project workflows.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.

Develop frameworks and tooling to estimate long-term impacts of product changes from short-term experiment outcomes. Build causal inference methods (experimental, econometric, quasi-experimental), evaluate heterogeneous treatment effects, own major estimation problems, and communicate results to technical and non-technical stakeholders to drive better long-term decisions.

Lead development of scalable analytic solutions that transform complex intelligence data into actionable insights. Design and implement data pipelines, automation, data models, visualizations, and production-ready Python tools. Provide technical leadership, rapidly prototype solutions, and collaborate with multidisciplinary IC teams to modernize analytic workflows and support operational decision-making.

Develop and operationalize graph-based analytics for fraud detection and investigative analysis. Build graph models, apply graph algorithms and ML to detect anomalies, support entity resolution, create visualizations and dashboards, and collaborate with investigators and stakeholders to deploy and maintain production solutions.

Lead end-to-end data science for a priority vertical (Insurance), building, validating, and deploying ML models that drive ROAS and lead quality. Partner with stakeholders and ML engineering to monitor live performance, iterate models, and identify new modeling opportunities to improve revenue and media efficiency.

The Senior Data Scientist will build frameworks for data-driven decision making, develop predictive models, ensure data quality, and enhance customer experiences through analytics and operational insights.

Lead analytical efforts in Real World Data research, develop specifications and methodologies, manage projects, and communicate findings to stakeholders.

The Sr. Football Data Scientist will build, deploy, and refine analytical models for football insights, managing large datasets and collaborating with teams for high-quality analytics solutions.

The Data Scientist will enhance statistical models for forecasting, analyze datasets for operational planning, and communicate insights to stakeholders.

The Data Scientist designs and builds data structures for AI applications, manages vector and feature stores, leads data discovery, and collaborates on responsible data use while ensuring high-quality, governed datasets for ML models and applications.

This role involves leading data science projects, designing technical solutions, implementing machine learning methodologies, and developing data workflows for cybersecurity applications.

The Data Scientist II will develop machine learning algorithms, architect data solutions, and support cybersecurity initiatives by analyzing complex data sets.

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

Develop and refine predictive models, perform exploratory data analysis, and generate AI-driven insights for DoD missions. Integrate multi-source data, apply geospatial analytics, iterate models via Agile sprints, produce performance and cost reports, and optimize analytic workflows for operational deployment.

Design, develop, and deploy ML models and data pipelines on large structured and unstructured datasets for geospatial intelligence. Build visualizations, integrate analytics with program systems, evaluate model performance, and mentor mid-level data scientists while collaborating with mission analysts to deliver production capabilities.

Lead design and delivery of generative and predictive AI solutions, build evaluation pipelines and responsible AI practices, assemble and model enterprise datasets, translate business needs into deployable AI products, and communicate results to stakeholders while driving R&D for GenAI and agentic AI in healthcare.

The Data Scientist will analyze data for risk and trading in Daily Fantasy Sports, develop predictive models, and monitor real-time data.

Develop and maintain predictive pricing, retention, and conversion models using GLMs and advanced analytics; build monitoring tools for rating performance; support rate filings, product pricing analyses, competitor research, and cross-functional collaboration to inform pricing and enterprise risk decisions.

Lead data science strategy across complex customer engagements, turn ambiguous business problems into repeatable ML solutions, define modeling and evaluation standards, drive data readiness and MLOps practices, mentor teams, produce reusable IP, and represent Microsoft in executive and thought-leadership forums.

Lead design and deployment of machine learning and quantitative analytics for AML, fraud, sanctions screening, transaction monitoring, and customer risk scoring. Build end-to-end model pipelines, validate and monitor model performance, support model governance and regulatory reviews, collaborate with compliance and engineering teams, and mentor junior data scientists.

Lead development and deployment of ML models and analytics for aircraft predictive maintenance. Build robust ETL pipelines, perform EDA and statistical analysis, collaborate with domain experts, integrate models into applications/APIs, and monitor model performance in production.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.

Design, build, and scale detection and diagnostic ML models for PV and storage time-series data; own model lifecycle from problem definition to production and monitoring; partner with engineering and operations to deploy models and measure field impact; prototype new approaches and guide technical roadmap.

Lead high-impact analytics projects from problem scoping to production: develop statistical and ML models, build production-grade Python pipelines and web apps (Streamlit/Dash), deploy on cloud (AWS/Databricks), and deliver actionable recommendations to stakeholders and executives.