The 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 Senior Data Engineer is a key technical leadership role to enable application of advanced analytics techniques across clinical development continuum. Enables Data flow, Data Wrangling, and Data Modeling in support of the advanced analytics techniques like Machine Learning and Visual Analytics. Drives standards and best practices to ensure consistency.

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 with in Clinical Development
• Build data products and service processes which perform data transformation, metadata extraction, workload management and error processing 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, and techniques and propose improvements to the data pipeline
• Develop data set processes for data modeling, mining and production
• Develop solutions to improve data reliability, efficiency and quality
• Ensures adherence to federal regulations and applicable local regulations, Good Clinical Practices (GCPs), ICH Guidelines, AbbVie Standard Operating Procedures (SOPs), and to functional quality standards. Stays abreast of new and/or evolving local regulations, guidelines and policies related to clinical development
• Partner with Data Scientists to understand model input requirements and translate them into reliable, reproducible data pipelines with appropriate QC and validation steps
• Manage and govern clinical trial data assets across the data lifecycle, ensuring traceability, reproducibility, and compliance with applicable data standards (CDISC/SDTM)