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Intelligent Automation Associate Director
3 months ago
- Automation Architecture: Design, develop, and deploy intelligent automations tailored for R&D functions such as Research Informatics, SDMS, Lab Management Systems, Pathology, Imaging, Pharmacology, and Development Operations.
- Integration and Development: Integrate automation with AWS Data Lakes, big data, analytics, AI, and machine learning. Use tools like RPA, Power Platform, UiPath, Blue Prism, and Automation Anywhere to streamline processes in Clinical Development Operations.
- Optimization: Leverage microservices architecture for intelligent scheduling, process management, and cost optimization. Apply innovative solutions to elevate automation from a tactical to a strategic capability.
- Collaboration: Work closely with business stakeholders, analysts, engineers, and other IT&D teams to understand automation requirements and deliver high-quality solutions. Ensure alignment with business objectives and empower scientists and researchers in R&D.
- Continuous Improvement: Implement ALM, DevOps, continuous integration, and deployment. Monitor, troubleshoot, and maintain automations to ensure reliability, scalability, and performance.
- Innovation: Stay updated on the latest trends in intelligent automation, AI/ML, cloud computing, and big data architecture. Participate in the Architecture Community of Practice to drive improvement and innovation across the organization.
- Team Leadership: Lead a skilled team of Automation Engineers to achieve architectural and automation engineering excellence.
- Master's degree in Computer Science, Information Systems, Information Technology, Computer Engineering, or a related field is preferred.
- Bachelor's degree in Computer Science, Engineering, or a related field.
- At least 5 years of experience in automation development, testing, and deployment using cloud-based platforms and tools.
- Proficiency in programming languages like Python, Java, and RPA tools such as Power Platform, UiPath, Blue Prism, or Automation Anywhere.
- Knowledge of cloud computing concepts and services such as AWS, Azure, or Google Cloud Platform.
- Experience in applying AI, ML, and big data techniques to automate complex business processes.