Top Remote Data & Analytics Jobs in Orlando, FL

Leads enterprise strategy, architecture, delivery, governance, and operations across data, analytics, information management, automation, AI, and GenAI. Drives transformation toward governed data foundations, reusable data products, data marketplaces, self-service analytics, and responsible AI. Oversees platforms, pipelines, dashboards, automation initiatives, budgets, vendors, and senior technical teams while partnering with business, Legal, Compliance, Quality, and Security. Builds training, adoption, stewardship, and change-management programs across a global life sciences organization.

Analyze operational, payment, enrollment, survey, and field data to identify fraud risks and anomalies. Design and validate fraud-risk indicators, support investigations, improve data quality, and translate findings into actionable recommendations. Partner with Product, Data Engineering, and operational teams to develop fraud-detection tools and improve controls. Document reproducible, audit-ready analysis while communicating uncertainty and findings clearly to nontechnical stakeholders.

Own risk analytics initiatives that improve repayment rates, optimize collections costs, monitor trends and emerging risks, and evaluate external agency performance. Partner with stakeholders and vendors to develop actionable insights, support strategic decisions, and apply AI tools to automate reporting and improve collections outcomes. The role requires advanced SQL and experience in credit risk, fraud risk, collections, or related lending analytics.

Supports IAA’s vehicle enhancement and make-ready program through recurring dashboards, baseline metrics, cost and vendor analysis, SLA tracking, workflow documentation, and process improvement. Identifies trends, risks, bottlenecks, and billing inefficiencies while preparing actionable recommendations and leadership materials to improve margin, cycle time, seller experience, and operational execution across branches and regions.

Provide analytics, automation, and reporting to support compliance testing and monitoring. Collect, validate, reconcile, and analyze large data populations; develop repeatable testing routines, dashboards, and automated solutions; support sampling, exception analysis, reporting, and stakeholder requests to identify trends, risks, and remediation status.

Leads and develops a team of Conversion Data Analysts, owning workload prioritization, delivery quality, and performance outcomes. Uses KPIs and data to identify risks, guide decisions, and improve high-volume conversion workflows. Partners with project management, Engineering, Product, Support, and leadership to ensure accurate data mapping and successful customer onboarding. Requires people leadership, SQL, Excel, data validation, CRM, and conversion tooling experience.

Analyze operational data, build and maintain reporting models, KPIs, dashboards and scorecards. Provide actionable insights to improve network performance and customer experience. Prepare presentations for leadership, collaborate with cross-functional teams, and recommend operational efficiency improvements while maintaining BI best practices and continuing education.

Supports reporting, data analysis, dashboard development, data collection, quality assurance, and data warehousing initiatives. Cleans and analyzes data, generates actionable insights, communicates findings to nontechnical stakeholders, responds to data requests, and partners with KIPP program teams. The role also contributes to network events, team operations, and equity-focused culture development.

Supports IAA’s vehicle enhancement and make-ready program through recurring dashboards, baseline metrics, vendor and cost analysis, workflow documentation, process improvement, and leadership reporting. Tracks volume, cycle time, aging, completion, exceptions, SLA performance, utilization, and cost variances to identify operational risks and recommend improvements that support efficiency, margin, seller experience, and GTV growth.

Designs and scales cloud-native data platforms and ingestion pipelines using AWS, Databricks, and Spark. Builds APIs, file-upload mechanisms, ETL/ELT workflows, data transformations, and Lakehouse architecture supporting analytics, machine learning, and operational use cases. Provides operational support for legacy C# .NET ingestion processes, troubleshoots pipelines, and promotes data governance, security, reliability, and documentation in a multi-tenant SaaS environment.