Top Remote Data & Analytics Jobs in Dayton, OH

Partner with Product leadership to build scalable Snowflake data models, run behavioral and funnel analyses, design A/B tests and predictive models, and translate insights to influence roadmap and product strategy.

Lead development of provider data products and ML-based decision support, manage provider data quality, collaborate across teams to build provider-facing analytics, and establish MLOps standards and governance.

Design, implement, optimize, and maintain scalable, high-availability database solutions for a SaaS healthcare product. Integrate client data, resolve complex data issues, refactor systems for Azure, optimize MSSQL performance, participate in code reviews, and deliver features within Agile/Scrum to ensure reliable, high-performance data services.

Build and maintain Salesforce reports/dashboards, analyze marketing and funnel performance, partner with Marketing/RevOps, experiment with AI-driven data/enrichment workflows, ensure data quality across systems, and present insights to leadership.

Lead the data organization to define data strategy, build scalable cloud-native pipelines, enable self-service analytics, drive AI/ML initiatives, and manage a multidisciplinary team to inform product, clinical, and business decisions.

The Stop Loss Claims Analyst reviews, analyzes, and adjudicates stop loss claims, ensuring proper adherence to policy terms and guiding clients through complex claims.

As a Data Engineer at LiveKit, you will manage the analytics infrastructure, develop scalable GCP-based data pipelines, and ensure effective data movement and transformation processes, while collaborating with the Analytics and engineering teams.

Lead statistical programming for clinical trials, oversee programming deliverables, ensure compliance with CDISC standards, supervise staff, and develop programming solutions.

The Sr Data Enablement Specialist will enhance data governance, support BI tools, implement AI workflows, and provide training resources to improve data utilization across teams.

Partner with customers and sales to design and present scalable Databricks Lakehouse architectures, build reference architectures and demos, consult on big data, ETL, and ML projects, validate integrations, and drive technical wins and adoption across accounts.