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Position: Manager, Statistics
Institution: Takeda Pharmaceutical Company Limited
Location: Boston, Massachusetts, United States
Duties: Applying advanced AI and other data science methodologies (statistics, machine learning, deep learning) as needed for various projects and data types; Effectively working with and gleaning insights from a wide variety of data sources, such as wearable device data (for example accelerometry and ECG), voice recordings, imaging data, or preclinical data; Developing bespoke data analysis pipelines by applying state-of-the-art data manipulation and analysis tools in a variety of programming languages
Requirements: Ph.D. in a field such as Biostatistics, Physics, Electrical Engineering, Biomedical Engineering, Computer Science, Applied Mathematics with some relevant experience (including internships and research assistantships), or Master’s degree with a minimum of 3 years of relevant experience; Expert-level knowledge of AI/deep learning methodologies and architectures; Expert-level knowledge of data science programming languages (Python and R, or similar) and experience with recommended practices for software development
   
Text: Manager, Statistics Applying advanced AI and other data science methodologies (statistics, machine learning, deep learning) as needed for various projects and data types; Effectively working with and gleaning insights from a wide variety of data sources, such as wearable device data (for example accelerometry and ECG), voice recordings, imaging data, or preclinical data; Developing bespoke data analysis pipelines by applying state-of-the-art data manipulation and analysis tools in a variety of programming languages Ph.D. in a field such as Biostatistics, Physics, Electrical Engineering, Biomedical Engineering, Computer Science, Applied Mathematics with some relevant experience (including internships and research assistantships), or Master’s degree with a minimum of 3 years of relevant experience; Expert-level knowledge of AI/deep learning methodologies and architectures; Expert-level knowledge of data science programming languages (Python and R, or similar) and experience with recommended practices for software development
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