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

The Senior Data Analyst will lead efforts to address veterinary data challenges through analysis and reporting, collaborating with clients and product teams to create impactful insights and deliverables.

The Associate Director of Biostatistics leads statistical methodologies for clinical trials, collaborates with study teams, ensures data quality, and prepares detailed reports while focusing on oncology studies.

The Director of BI & Analytics will lead data architecture, build a data lake, manage ELT/ETL pipelines, and develop enterprise reporting and models, partnering across functions to drive commercial analytics and insights.

The Associate Director will lead BI&A research projects, translate data into strategic insights, and communicate findings to senior leadership within a fast-paced biopharmaceutical environment.

The Market Access Analytics Director oversees Market Access KPI reporting, analyzes payer performance, and enhances data assets while collaborating across teams to inform decision-making and strategies in market access analytics.

The LMS Administrator will manage CareDx's Learning Management System, ensure compliance training deployment, conduct audits, and provide stakeholder support, focusing on system optimization and collaboration across departments.

Responsible for executing Biostatistics strategy through collaboration on clinical study documents, oversight of vendors, and innovative statistical methodology in a biotech environment.

The Head of Biostatistics and Statistical Programming oversees statistical aspects of clinical trials, ensuring integrity and strategic planning in drug development. Responsibilities include leading teams, guiding analysis, mentoring, and collaborating across departments.

Lead statistical programming for clinical trials, ensuring timely and accurate statistical deliverables. Oversee CROs and mentor junior programmers, adhering to regulatory standards.

As a Senior Data Scientist, you will develop statistical models and machine learning algorithms to improve patient enrollment and trial management while ensuring compliance in a healthcare data environment.