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Supervise the development and deployment of Robotic Process Automation (RPA) and PowerApp solutions to digitally enhance operational efficiency. data storage, database infrastructure, and data engineering) to maintain efficient and secure management of data assets as well as application hosting.
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IT-infrastructure, data security and project management tools (AWS, Dynatrace, CyberArk, Azure DevOps, Confluence, Jira, etc.) Concepts of process automation and digitization (e.g. RPA.
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As a member of our Machine Learning Research team, you will design new approaches to derive insights from Pfizer's proprietary data and external datasets to generate testable hypotheses across the drug discovery continuum with a focus on extracting biological knowledge.
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Through this opportunity, you will work with a multi-disciplinary team consisting of engineers, scientists, and clinicians to process large biomedical datasets, develop and evaluate machine learning algorithms, and implement data visualization tools for advanced medical imaging technologies.
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Experience with analytic methods and technologies (artificial intelligence, machine learning/ deep learning/ neural networks, natural language processing, visualizations) Coordinate with data management study leads, clinical operations, internal stakeholders and external vendors to map non-EDC data from collection through analysis.
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Analytical Skill, Analytical Skill, Analytical Thinking, Chemical Biology, Chemistry, Clinical Trials, Communication, Curiosity, Data, Data Analysis, Design of Experiments (DOE), Detail-Oriented, Drug Development, Drug Discovery Process, Ethics, Gene Therapy, Group Problem Solving, Immunology, Inventory Management, Laboratory, Large Group Presentations, Leading Project Teams, Life Science, Machine Learning (Ml), Medical Research {+ 9 more.
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Experience working in an outsourced data management model preferred. Ability to successfully contribute to improving the quality of internal data management policies, programs and initiatives.
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PHD candidates in Engineering/ IT/Computer Engineering/Computer Science/Bioinformatics or related discipline with relevant with a minimum of 1 years of experience in software development and pipeline automation in the pharmaceutical and/or biotech industry Required Skills Engineering skills to be able to optimize data analysis workflows for speed and usability Proficiency in programming languages such as Python and R, with experience in data analysis and machine learning libraries.
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The Ethics & Compliance Manager Position reports directly to the Ethics and Compliance Director, who reports to the VP of Enterprise Risk Management & Compliance (including Data Privacy and Protection.
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This involves the development of a control system architecture, selecting the components to be used, detailing schematic diagrams and system documentation, programming controllers (PLC, DCS, RTU), programming the process visualization (HMI/SCADA), developing databases for archiving/displaying historical system data and implementing/commissioning the control system at the client’s facility.
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The Human-centered AI team in Digital Health Oncology R&D is looking for an Associate Director of Data Science to specialize in the development of novel digital endpoints using data science, statistical learning, and innovative machine learning methods on multi-modal datasets including clinical data, real world evidence, imaging, wearables, and other biomedical data sources to address patient burden.
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Analytical Skill, Analytical Skill, Analytical Thinking, Chemical Biology, Chemistry, Clinical Research, Clinical Trials, Collaboration, Communication Skills, Curiosity, Data, Data Analysis, Design Of Experiments, Detail-Oriented, Diversity & Inclusion, Drug Discovery, Ethics, Gene Therapy, Immunology, Laboratory, Lifesciences, Machine Learning (Ml), Mammalian Cell Culture, Medical Research, Molecular Biology {+ 11 more.
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The project aims to develop a multimodal AI framework to analyze the physiological signals and to decipher underlying molecular mechanisms by exploring other data sources, such as omics, large databanks, real world evidence, and clinical information.
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This candidate should have experience in at least one area of deep learning but have familiarity with a broad array of techniques, such as data pipeline development, metabolic engineering, or protein engineering.
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Background in applied or pure mathematics, statistics, bioinformatics, machine learning or computational biology; interest in cancer biology and work experience in computational biology and/or biostatistics are preferred.
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machine learning data management robotic process automation jobs Title: director in Lexington, MA
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