Top Analysis Reporting Jobs in New Orleans, LA

The CAT Pricing Lead will manage catastrophe pricing strategy, integrating risk insights into pricing, and leading a team focused on actuarial analysis and portfolio risk management.

The Central Risk Manager is responsible for developing and implementing strategies for Risk-Based Quality Management and Monitoring across clinical trials, collaborating with stakeholders, and leveraging data analytics for risk identification and mitigation.

Senior individual contributor owning partnerships analytics: lead agent acquisition and product analytics, build risk/commission debt models, define onboarding "aha" metrics, optimize funnels, enable self-serve analytics, integrate generative AI tooling, and mentor junior analysts.

As a Memory Developer, you'll record household tasks, shape the robot's memory, and help train its AI for better performance.

As an Analytics Engineer, transform raw data into metrics, manage data pipelines, ensure data integrity, and collaborate with teams for analytics solutions.

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

The ETL Developer will design, develop, and test ETL scripts using the Informatica platform, gather requirements, and maintain database applications. They will also provide end-user support post-deployment and ensure requirement fulfillment.

The Senior Korean Linguist manages translation and copy-editing tasks, coordinates with freelancers, and ensures high-quality Korean-language deliverables while improving workflows and supporting quality efforts.

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.

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks and AWS S3; manage large geospatial and temporal datasets; productionize ML models; implement data validation, testing, and monitoring; optimize storage and processing; document workflows; collaborate cross-functionally.