Top Remote Data & Analytics Jobs in Houston, TX

The Data Analyst will maintain KPI standards for Customer Support, build dashboards, produce analyses, and lead metric alignment conversations, ensuring accurate data interpretation and decision-making support.

Build dashboards and reports tracking enrollment, attendance, engagement, and attrition. Develop predictive models to identify at-risk students, analyze withdrawal causes, monitor early-warning indicators, partner with cross-functional teams to design retention interventions, and present insights to leadership while supporting data pipeline accuracy.

The Director of Data and Enterprise Applications oversees the architecture, development, and support of business applications and data platforms, driving digital transformation and ensuring data integrity and accessibility.

The Analytics Lead will guide data-driven decision-making, develop measurement frameworks, forecast revenue, and mentor an analytics team, ensuring effective communication of insights.

Design and scale data infrastructure, ensure data quality and governance, collaborate with teams on data products, and modernize practices.

The Senior Data Scientist will lead analytics efforts, develop data models, and improve data pipelines to support Thyme Care's mission in oncology care, while collaborating with stakeholders to enhance performance metrics and reporting.

Lead Oracle Cloud reporting initiatives, ensuring data accuracy and performance while mentoring junior analysts and collaborating with stakeholders.

Lead design and development of web UI and REST middleware within an Agile team. Define requirements and acceptance criteria, implement Java/Spring and Angular solutions, build CI/CD pipelines, work with Product Owner and testers, and produce/update design documentation.

Design and run evaluation frameworks to assess third-party vendor data for fraud detection. Build lift analyses, backtests, and champion/challenger tests; profile vendor datasets for quality and coverage; collaborate with fraud, product, and data engineering to define ingestion and testing requirements; translate findings into actionable recommendations and maintain an internal knowledge base; support ad hoc analyses on fraud trends and model performance.

The Data Engineer builds and maintains data infrastructure, develops data pipelines, ensures data quality, and collaborates with teams for insights.