Top Remote Data & Analytics Jobs in Philadelphia, PA

As a Senior Associate, you'll lead the development of Salesforce Marketing Cloud solutions, mentor junior members, analyze complex problems, and build strong client relationships while ensuring high-quality standards.

The Senior Analyst in AML KYC will conduct detailed customer onboarding, monitor client relationships for compliance, and update KYC records while utilizing AI tools for efficiency.

Own fraud detection and mitigation for ACH payments: design controls, run investigations, analyze performance (precision/recall, financial impact), and partner with Product, Data Science, Risk ML, and Engineering to operationalize risk strategies and transition to model-driven controls.

As a Healthcare Analyst II, you will analyze healthcare data to optimize strategies, conduct quantitative analysis, and provide insights for clinical interventions, partnering with cross-functional teams.

The Analyst II will analyze data to optimize fraud strategies, develop machine learning models, and collaborate with teams to enhance product safety.

Manage Finance-focused data products, collaborating with teams to define requirements and drive delivery for financial analysis and reporting.

Lead strategic vision and execution of GitLab's data platform and analytics capabilities, focusing on AI-enabled self-service analytics and data governance.

Lead company-wide data governance and standards, enforce data contracts and schema/version control, manage PII/RBAC and master data, establish data-quality metrics and monitoring, grow and run the governance team, remain hands-on with data profiling and SQL, and use AI to accelerate governance work and modeling.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

Design, build, and maintain scalable dimensional data models, semantic layer, and production transformation pipelines. Collaborate with Data Engineering, Data Science, BI, DevOps, and Product to enable AI-ready data, ensure data quality and governance, support ML feature delivery, and coach analytics team members for robust, production-ready data products.