Top Biotech Data & Analytics Jobs in Austin, TX

Responsible for managing all data management activities for clinical studies, including data collection, cleaning, reporting, and validation, while ensuring regulatory compliance and quality data delivery.

The Logistics Analyst optimizes logistics operations through advanced analytics, process improvements, and automation, partnering cross-functionally to enhance service performance and data reporting.

The IT Compliance Manager oversees compliance activities, ensuring adherence to regulations like SOX and GDPR, conducting audits, and developing compliance policies.

The Epic Integration Analyst focuses on implementing and optimizing EMR integrations, particularly with Epic Aura, ensuring data exchange quality and interoperability with external healthcare systems.

The Senior Business Systems Analyst analyzes and optimizes systems, gathers requirements, collaborates with stakeholders, implements technology solutions, and manages change to improve efficiency.

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

The Business Intelligence Analyst II will enhance data literacy, support stakeholders in data access, and deliver analytical insights while fostering relationships with decision-makers.

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 Biostatistician II will oversee clinical trials, manage statistical deliverables, lead client interactions, and mentor junior staff.

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.