Top Senior Analysis Reporting Jobs in Indianapolis, IN

The Senior Healthcare Analyst will analyze healthcare data, provide insights to improve clinical programs, and collaborate with multidisciplinary teams to drive program effectiveness and optimize patient outcomes.

The Sr. Operations Analytics Manager will enhance billing and account efficiency, analyze complex data, and provide actionable insights to improve financial performance and customer service.

Lead and grow a casino analytics team to measure, diagnose, and optimize Casino revenue performance. Provide strategic analytics support to Revenue Operations, run deep-dive customer and promotion analyses, build scalable monitoring/alerting frameworks, translate insights into actionable recommendations, and manage an analytics roadmap prioritizing short-term wins and long-term AI-enabled strategies.

As a Product Analyst, you will analyze customer behavior and product performance, create dashboards, and support decision-making through data insights and metrics definitions, while collaborating across teams.

As a Senior Data Analyst, you will analyze marketing performance, implement multi-touch attribution, and provide insights for strategic decisions.

The Senior Data Scientist will develop and deploy analytical models supporting marketing strategies, assess campaign effectiveness, and optimize customer engagement strategies, employing advanced data-driven methods and partnering with marketing stakeholders.

Lead analytics implementations using Google Analytics and Tag Manager for clients, providing mentorship and managing projects with a focus on enhancing clients' digital strategies.

The Senior Data Engineer II will design and maintain scalable data pipelines, collaborate with teams on machine learning workflows, and ensure data accessibility across systems.

The Market Risk Analyst will monitor SBL lines of credit, assess market risks, manage collateral, develop risk models, and prepare reports.

Lead the ML Data Enablement team to define strategy and execution for accelerating third-party and internal data onboarding, enforce data quality and SLAs, build metadata and lineage standards, and deliver scalable data infrastructure and workflows to reduce time-to-production for ML models.