Top Remote Data Science Jobs in St. Louis, MO

Lead data-science projects with customers to acquire and prepare data, develop and evaluate production-scale models, drive model best practices, contribute thought leadership and open-source solutions, and define business and solution strategy while collaborating across engineering teams.

Conduct data science research on experimental methodologies and causal modeling, develop tools to analyze experimental data, prototype improvements to platform features, and communicate results to internal teams and external stakeholders to drive product and research decisions.

Design, build, and productionize complex Bayesian and probabilistic modeling pipelines. Lead methodology and technical direction, collaborate with Product/Data/Engineering, mentor data scientists, communicate results to stakeholders, and apply hierarchical, state-space, time-series, and graphical modeling at scale with strong emphasis on uncertainty and model validation.

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Lead research defining fabric's AI shopping roadmap: design and run quantitative studies, build models and benchmarks for AI search (AEO/GEO), run experiments, translate findings into customer-facing insights, and publish research to influence product and go-to-market strategy.

Activate and operationalize clinical data for analytics by designing data models, taxonomies, and a clinical feature store; build reusable data assets, documentation, and dashboards; ensure data quality and compliance; partner with stakeholders and data engineering to enable downstream analysts, data scientists, and business users.

Lead data science across Growth and Membership Operations: define problems, build scalable data models and pipelines, develop analytical and ML solutions, partner cross-functionally, and integrate AI tools to optimize sales, marketing, telesales, and enrollment operations.

Design, build, and deploy search and recommendation ML systems (ranking, retrieval, personalization, query understanding) using embeddings and LLM approaches. Collaborate with engineers and product to create scalable data pipelines, integrate models via APIs, run A/B tests, and monitor online/offline metrics to improve relevance and engagement.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

Lead advanced analytics and ML for partner-facing teams: build end-to-end predictive models (churn, lead scoring, health scores), marketing attribution and mix models, and maintain ML lifecycle standards. Develop agentic workflows and Claude Skills to accelerate work, mentor analytics team, and translate model outputs into actionable insights for Sales, Marketing, and CX.

Design, iterate, and productionize ML models for recommendation, ranking, routing, and monetization. Lead A/B testing and causal measurement, build dashboards and analytics, define metrics, collaborate with Product and Engineering, and scale ML/analytics infrastructure and model lifecycle management.

The Senior Associate Scientist will analyze and visualize data, collaborate on screening strategies, and validate hits for drug discovery using the EMMI platform.

The Senior Data Scientist will design and deploy machine learning models, analyze complex datasets, develop analytical solutions, and mentor junior team members in a remote environment.

The Geospatial Scientist leads data management initiatives, oversees data governance, and ensures quality and compliance of geospatial data for DHS.

Build, deploy, monitor, and document predictive models and analytical reports; translate findings for non-technical stakeholders; ensure model quality, fairness, and explainability; collaborate with data engineers and train staff on data literacy; support production ML infrastructure on AWS.

Design and validate clinical and biomarker data pipelines, perform source-to-target mappings (e.g., EHR to data model), extend ontologies and vocabularies, ensure data quality, evaluate informatics tools, and support data architecture and analysis workflows for a regulatory-grade platform.

Lead development of digital twin and simulation models for warehouse operations, deploy AI-driven decision systems, build forecasting and optimization models, validate experiments linking operational metrics to financial impact, and partner with engineers, operations, and external AI/academic partners to scale innovations across fulfillment networks.

Develop, implement, and maintain ALM and CCAR/PPNR models for balance sheet forecasting. Build customer behavior, prepayment, and deposit models; monitor performance, perform backtesting and sensitivity analysis; collaborate with Model Risk, Market Risk, Treasury, and validation teams; prepare documentation and materials for governance and senior leadership; support balance sheet optimization and regulatory initiatives.

The Senior Data Scientist will analyze customer data, design experiments, build models, and support data-driven decision-making across product, marketing, and customer experience teams.

As a Lead Data Scientist, you will shape data strategy, develop predictive models, analyze healthcare datasets, and collaborate with teams to enhance MDCalc's clinical decision tools.

Lead retention and churn efforts by building survival and lifecycle-state models, classifying churn reasons, creating personalized win-back and next-best-action interventions, optimizing offers/promos, designing uplift experiments, and owning production model lifecycle, monitoring, and MLOps across Product, CRM, Marketing, and Engineering.

The Principal Data Scientist will design, develop, and deploy machine learning models, innovating methodologies and collaborating across teams to enhance financial decision-making and risk management.

The Senior Data Scientist leads data-driven initiatives, optimizing analytics for business decisions, mentoring staff, and ensuring data quality across healthcare projects.

As a Senior Data Scientist, you will identify business opportunities, build machine learning solutions, partner cross-functionally, and translate analyses into actionable insights.

Lead development of graph-based identity resolution for CTV advertising: design models and validation, analyze large identity datasets with distributed tools, productionize data-science workflows, partner with engineering/product, and apply privacy-by-design while leveraging LLMs and AI-assisted development.