Top Data & Analytics Jobs in Omaha, NE

Lead custom analytics and ML model development for non-prime lending using traditional and alternative data. Scope and deliver credit strategies (underwriting, pricing, collections), engineer features, evaluate data sources, present insights to clients, and support model implementation, monitoring, and optimization.

The role entails developing and executing pricing strategies to enhance revenue for Upwork, alongside conducting extensive market and data analysis to inform pricing decisions.

The Data Solutions Engineer designs and operates scalable data pipelines using Azure tools, ensuring data quality and efficient analytics for BI Development.

The Marketing Data Analyst will report on marketing performance, create actionable insights, and support analytics for data-driven decision-making. Responsibilities include data reporting, accuracy, and collaboration with the marketing team to develop dynamic dashboards and forecasts.

As a Data Scientist, you'll empower the company with data insights, develop metrics and experiments, and collaborate with various teams to drive product decisions.

As a Revenue Intelligence Architect, you'll build the revenue intelligence stack, including data architecture and AI workflows, to drive growth and predictability in revenue for a modern SaaS platform.

The role leads the People Analytics and AI transformation at WelbeHealth, integrating strategy and technology to enhance workforce decision-making and analytics delivery.

The Staff Data Analyst will build Boulevard's product data foundation, create data-driven recommendations, develop dashboards, conduct deep analyses, and foster a data-driven culture within the organization.

The Data Analyst 2 will partner with various teams to optimize workforce strategies through data analysis, visualization, and reporting. Key responsibilities include extracting data, developing dashboards, and collaborating to inform business decisions.

Design, build, and operate resilient batch and streaming data pipelines. Own datasets end-to-end. Collaborate with data science teams and define data quality standards.