Top Senior Data Science Jobs in St. Louis, MO

As a Senior Data Scientist in Marketing Analytics, you'll partner with Marketing to solve problems, design data-driven solutions, lead experimentation, communicate insights, and operationalize solutions for business impact.

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

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

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

As a Staff Data Scientist for Forecasting, you will develop forecasting algorithms, create data pipelines, mentor team members, and operationalize models for business needs.

As a Staff Data Scientist, you will develop data-driven fraud prevention solutions using Generative AI, design scalable ML pipelines, and mentor junior data scientists while collaborating across teams to improve analytics and decision-making.

The Senior Data Scientist will lead innovative data science efforts, leveraging advanced analytics, visualization, and insights from complex datasets to boost productivity.

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.

Lead and manage data science teams to build models and drive business solutions; oversee compliance analytics and ensure data quality.

The Senior ML Data Scientist will develop machine learning models to detect fraud, manage the data science lifecycle, and collaborate with teams on strategies.

Build and own measurement systems, MMM, incrementality, and experimentation frameworks to quantify marketing incrementality, optimize budget allocation, forecast LTV/CAC, and operationalize production-grade models and metrics to drive growth, retention, and long-term enterprise value.

Lead design and ownership of advanced fraud detection models and monitoring. Apply adversarial threat modeling to select architectures (tree-based, deep, graph), debug production failures, run experiments, mentor engineers, and build AI-assisted workflows that scale domain expertise while resisting manipulation.

As a Data Scientist, you will develop advanced fraud detection models, collaborate with teams, analyze data for insights, optimize models, and ensure compliance with data privacy.

As a Business Intelligence Data Scientist, you will develop dashboards, build statistical models, and analyze product features to enhance user insights.

The Staff Data Scientist will oversee Foodsmart's member growth analytics, focusing on marketing and product funnel optimization, experimentation, attribution modeling, and enhancing self-service analytic tools while collaborating closely with leadership and cross-functional teams.

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, deploy, and operationalize analytical and ML/AI models for cybersecurity use cases. Collaborate with product and engineering teams to enable and migrate production workflows, work with databases and data architectures, create visualizations and reports, adopt new cloud orchestration and data virtualization technologies, and mentor junior cyber data scientists.

This role involves partnering with sales to design data-driven solutions, engaging with customers, and supporting pre-sales engagements.

As a Staff Data Scientist, you'll develop and deploy machine learning models to enhance renter experiences and optimize property partnerships, collaborating across teams to translate data insights into actionable strategies.

Lead development and deployment of machine learning and LLM models, analyze large datasets, build PowerBI dashboards, mentor junior data scientists, and present findings to leadership while automating processes and improving model performance.

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.

Lead design and deployment of reinforcement learning and sequential decision models for collections and recovery. Build scalable ML pipelines (Databricks/Spark), run experimentation and offline policy evaluation, collaborate with engineering/MLOps to productionize models, and mentor junior data scientists.

Lead development of time-series and econometric models (ARIMA, VAR, state-space, WLS, panel data) and Bayesian probabilistic frameworks. Use causal inference and stochastic processes to produce actionable insights, work with large datasets on Databricks, communicate results to stakeholders, and mentor junior team members.

Design, build, and deploy production Generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, develop scalable FastAPI/async backends, containerize with Docker, and optimize GenAI systems for latency, cost, and reliability.

Design, build, and scale production Generative AI applications: multi-agent systems, advanced RAG pipelines, LLM integration and fine-tuning, scalable FastAPI backends, vector search, monitoring, and Dockerized deployments.