Top Data Science Jobs in St. Louis, MO

Lead design and deployment of ML systems for enterprise search, retrieval, recommendations, and AI agents. Build scalable data/ML pipelines, evaluation frameworks, and KPIs. Apply statistics, causal inference, experimentation, and analysis to guide product strategy. Prototype and productionize LLM-based features, mentor junior staff, and collaborate cross-functionally.

Deploy and validate NEXUS in customer environments, perform end-to-end data and feature engineering, benchmark against baselines (XGBoost/LightGBM), optimize models for business impact, handle last-mile integrations (VPC/on‑prem/air-gapped), and translate field insights into product improvements while liaising with stakeholders.

As a Senior Data Scientist, you'll optimize search relevance, retrieve content, analyze data, and collaborate with teams to enhance product strategies.

Build, monitor, and maintain production ML models and LLM/agent systems. Analyze complex datasets, develop proofs of concept, collaborate with stakeholders, and align work to sustainability business needs.

Build and deploy machine learning models for credit and fraud risk, analyze portfolio performance, monitor models in production, develop risk strategies, collaborate with engineering and cross-functional partners, and conduct high-impact ad-hoc analyses to inform business decisions.

Build and deploy end-to-end ML models for renter acquisition, supply optimization, ranking, personalization, and marketplace optimization. Lead technical projects from problem framing through experimentation, launch, monitoring, and iteration. Apply statistical methods, causal inference, and rigorous A/B testing. Partner cross-functionally, mentor other data scientists, and raise modeling and experimentation standards.

Build production AI retrieval and knowledge systems for rare disease research at NIH. Model biomedical ontologies in PostgreSQL, develop semantic search, retrieval-augmented services, ranking, evaluation, and observability, and deliver user interfaces with Next.js, React, and TypeScript. Deploy and operate services in on-premises and HPC Kubernetes environments, including GPU inference. Collaborate with NIH staff, clinicians, and researchers, and contribute to scientific publications.

Build and operate agentic AI systems for rare disease research, including multi-turn workflows, structured and validated outputs, tool calling, prompt testing, evaluation suites, asynchronous execution, streaming, observability, and failure handling. Collaborate with NIH staff, clinicians, geneticists, researchers, and patient communities to translate needs into reliable data models and system behavior. Own components through deployment, establish AI engineering standards, and contribute to scientific manuscripts, abstracts, and conference posters.

The role involves deploying data science solutions in cybersecurity, collaborating with customers and teams to operationalize models and enhance security insights.

Design, validate, and deploy scalable statistical and distributional models for enterprise consumer analytics. Implement rigorous methodologies, convert ad-hoc analyses into version-controlled analytical libraries, and partner with Data Engineering to optimize performance and scale compute workloads (including cloud/Databricks). Deliver reproducible analytics powering dashboards and LLM-driven applications, with a focus on panel and transaction-level consumer data.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

Lead end-to-end machine learning and advanced analytics projects: frame problems, build and deploy predictive/prescriptive models, design ETL pipelines, create dashboards and A/B test metrics, and deliver actionable insights to business stakeholders to improve fleet, pricing, and ridesharing products.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

Lead end-to-end development of fraud and risk ML systems: define problems, engineer features, build and evaluate models, collaborate with product and engineering, deploy with ML engineers, and monitor production performance to detect drift and trigger retraining.

Design, build, evaluate, and deploy predictive ML models across regression, classification, NLP, computer vision, and time-series problems. Perform data cleaning, feature engineering, EDA, model selection, tuning, explanation (SHAP, feature importance), and collaborate with engineers and stakeholders to operationalize and monitor models in production.

As a Data Scientist III, you'll build customer identity systems, develop recommendation models, predictive analytics for customer behavior, and collaborate with ML Ops and Data Engineering.

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.

As a Data Scientist, you will build predictive models, develop software tools, and analyze geoscientific data to support mineral exploration and discovery.

The Data Scientist will develop predictive models, analyze geophysical data, and collaborate with geoscientists to enhance exploration programs, focusing on mineral deposits.

As a Data Scientist, you will develop predictive models, analyze geoscientific data, and collaborate with geoscientists to improve mineral exploration efforts.

Design, build, and deploy production-grade machine learning models and analytical solutions. Work end-to-end from problem definition to production, write performant Python and SQL, resolve data quality issues, collaborate with product and engineering, support on-call, automate reporting, mentor junior teammates, and improve model reliability and workflows.

Develop autonomous AI agents capable of goal setting, decomposition, planning, tool use, memory, and long-horizon execution. Build evaluation frameworks for alignment, reliability, and safety; identify goal misalignment and undesirable behaviors; and integrate agent systems with APIs, databases, search engines, and other tools. Responsibilities include designing production-ready machine learning systems, collaborating across teams, and advancing agent autonomy through planning, causal reasoning, model-based reinforcement learning, and language modeling.

Owns the end-to-end development of cybersecurity vulnerability and exploit prediction models, including problem framing, feature engineering, training, evaluation, deployment, calibration, monitoring, and drift detection. The role addresses extreme class imbalance, hierarchical modeling, cold-start customers, and uncertainty quantification. It also requires explaining model outputs to security executives, publishing methodologies and benchmarks, and partnering with engineering to deploy reliable production systems.

Own end-to-end data science for a priority vertical (starting with Insurance): frame business problems, build and validate models, partner with ML engineering for deployment, monitor live ROAS performance, and drive measurable revenue and lead quality improvements.

Lead end-to-end data science projects including predictive modeling, algorithm design, and data pipelines. Build campaign-focused models (turnout, partisanship), perform exploration and visualization, translate insights into memos/presentations, collaborate with engineering to productionize models, mentor junior data scientists, and prototype emerging AI solutions for product impact.