Top Remote Data & Analytics Jobs in Houston, TX

The Business Operations Analyst will centralize and improve corporate reporting and business intelligence while executing data acquisition for patient data initiatives. Responsibilities include metric compilation, dashboard design, and data analysis across multiple teams.

Lead design, build, and optimization of Sezzle's large-scale data warehousing and pipelines. Own data infrastructure KPIs/SLAs, improve Redshift performance, evolve the data stack, implement ETL/ELT workflows, and partner with cross-functional teams to deliver reliable, scalable datasets.

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 Director of Analytics leads a team to generate insights using healthcare data, supports product development, and engages with clients, leveraging advanced analytics to inform decision-making.

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 Scientist will work with the team to build and optimize models, interpret data, and support product development using massive data sets for advertising effectiveness.

The intern will analyze customer engagement data from multiple sources to derive insights, support customer retention strategies, and present findings to leadership.

The IO Psychology intern will support data driven people initiatives in talent and organizational effectiveness, focusing on analytics and employee insights in a corporate environment.

The Senior Data Engineer will develop data engineering solutions, manage ETL workflows, and establish a new data practice while collaborating with stakeholders.

The DMS Data Engineer is responsible for analyzing raw data, building data systems and pipelines, conducting complex data analyses, and collaborating on projects to enhance data quality and reliability.