Senior Clinical Data Engineer, Hybrid ( IL-Based)

Company Description

AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas – immunology, oncology, neuroscience, and eye care – and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on X, Facebook, Instagram, YouTube, LinkedIn and Tik Tok.

Job Description

Data Science and Analytics is an ambitious team of Data Scientists and Data Engineers with a unique blend of Business, Scientific, and Machine Learning expertise that seeks to unleash the full potential of AbbVie’s data assets by bringing technology and/or data and insights to the forefront of decision making via fit-for-purpose solutions.

The Data Engineer role in DSA is a unique role with blend of data engineering responsibilities in the context of Data Science/ML and AI. A key role to enable application of advanced analytics techniques across clinical development processes. Drives analytics innovation and experimentation to enable data-driven insights for complex business problems across R&D by deploying advanced analytics techniques like Machine Learning and Visual Analytics.

Responsibilities:

• Collaborates with the Data Scientists and cross-functional data stewards to identify appropriate data sources and to enable streamlined data flow for the Data Science and Analytics capabilities across clinical development.
• Develop, construct, test and maintain architectures (such as databases and large-scale process systems) to support strategic projects within Clinical Development
• Build data products and processes which perform data transformation, metadata extraction, workload management and error processing and management.
• Implement standardized, automated operational and quality control processes to deliver accurate and timely data and reporting.
• Adhere to best practices for coding, testing and designing reusable code/component.
• Contribute to the discovery and understanding of new tools, techniques and technologies to change course as needed.
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