Top Analysis Reporting Jobs in Philadelphia, PA

The Quantitative Methodologist will design statistically rigorous studies, apply advanced analytics, interpret results, ensure quality, and communicate insights to clients.

The Senior Associate in Data Analytics & Insights will analyze complex problems, mentor team members, and translate requirements into actionable specifications, primarily in the healthcare domain.

The Principal Data Architect leads technical and business discussions on architecture direction, participates in enterprise strategic development, and manages architecture governance while aligning architectural requirements with IT strategy and business goals.

The Data Modeler will design and maintain data models, support data architecture, collaborate with product teams, and ensure quality and governance of data products.

The VP, Global Head of Statistics leads statistical strategy for vaccine clinical research, providing statistical analysis, overseeing a large team, and influencing cross-functional research efforts.

The Data Engineer is responsible for building and maintaining data pipelines, ensuring data quality and reliability, and collaborating with various teams on data needs. This involves working with AWS tools and programming in Python and SQL to support analytics and infrastructure.

Lead and manage the clinical statistical programming organization, oversee regulatory submission programming (datasets, TLFs) for global agencies, maintain SOPs and infrastructure, automate workflows (including AI), manage vendors/CROs, and mentor programming staff to deliver compliant, timely clinical deliverables.

As a Fraud Analyst, you will analyze fraud threats, ensure regulatory compliance, enhance internal processes, and develop effective reporting for management.

Lead the implementation and optimization of data governance for AI initiatives, managing policies, compliance, and cross-functional collaboration in Microsoft Azure environments.

The Lead Data Engineer will design and implement data solutions for imagery models, lead engineering efforts, collaborate with stakeholders, and ensure data quality and governance while optimizing data pipelines.