Top Analytics Jobs in St. Louis, MO

The Director, Analytics Infrastructure & Pipeline Operations is responsible for creating automated data pipelines and centralized feature stores for data science, leveraging AI technologies to optimize workflows and ensure data quality.

Lead implementations and analytics for community health centers: run stakeholder meetings, extract/clean/analyze EHR data, write SQL, build reports and visualizations, provide consultative support and training, manage data initiatives, and recommend product improvements.

Lead implementations and ongoing analytics consulting for community health centers: extract, clean, and analyze EHR data (heavy SQL), build reports and visualizations, manage projects and stakeholders, train client data teams, and inform product improvements based on customer feedback and domain expertise.

Lead implementations and provide consultative analytics for community health centers: extract, clean, and analyze EHR data (heavy SQL), build reports and visualizations, manage projects and stakeholders, train health center teams, and inform product improvements based on customer feedback and domain expertise.

Design, support, and improve data analytics solutions (OAC and Power BI). Manage data ingestion, ETL, data quality, access, and documentation. Liaise with stakeholders, migrate dashboards from OAC to Power BI, and follow SDLC and project management practices.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.

Manage end-to-end analytics projects, lead cross-functional teams, ensure client satisfaction, and utilize AI tools to enhance data insights.

Lead end-to-end product analytics for New Content Experiences, partnering with Product, Design, Data Science, and Engineering to analyze large datasets, build tools, ensure data quality, and inform product strategy and decision frameworks.

Analyze commercial data for sales, marketing, service and support to create metrics and insights for optimization. Collaborate with cross-functional teams to provide data-driven recommendations and manage business intelligence tools.