Top Remote Biotech Data & Analytics Jobs in New York City, NY

Lead statistical programming activities for clinical trials, manage project milestones, and collaborate with various departments to ensure high-quality deliverables.

As a Senior Product Analyst, you will analyze multimodal data, develop analytical tools, collaborate with teams, and define metrics for dashboards.

The Senior Director of Biostatistics at Gilead leads data analysis initiatives, directs cross-functional teams, and supports company priorities in statistical analysis and reporting. They ensure effective scientific communication and contribute to strategic departmental initiatives.

The Principal Statistical Programmer supports Epidemiology by preparing Real World data, conducting analyses, programming data tools, and liaising with stakeholders on multiple projects. Responsibilities include creating analytical databases, producing reports, and managing relationships with clients and investigators.

Lead statistical programming for clinical trials, managing programmers and CROs. Oversee programming activities, ensuring timely support for clinical projects and developing programming processes.

The Database Associate is responsible for data entry in the Clinical Trial Subjects database, troubleshooting issues, and assisting users with database functionality.

The Director of Biostatistics leads statistical strategy and analysis for clinical trials in oncology, collaborating with clinical teams and regulatory bodies.

As a Senior Data Scientist, analyze healthcare data to improve health outcomes, develop reports and dashboards, and collaborate across teams to leverage genomic data for health insights.

The Senior Director of Data Management leads data management strategy for clinical programs, overseeing CRO partnerships, data lifecycle management, and compliance with regulatory standards. They mentor staff and ensure high-quality clinical data for submissions.

The Senior Manager in R&D Data Literacy & Monetization drives the strategic utilization of R&D data, transforms datasets into reusable products, and promotes data literacy across the organization, ensuring effective governance and promoting advanced analytics and AI initiatives.