JOB DESCRIPTION
We are seeking a highly skilled Machine Learning and Deep Learning Scientist to join our team in advancing the development and analysis of biomarkers. This role involves pioneering the use of AI and advanced computational methods to explore large, complex datasets and uncover insights that drive translational medicine and therapeutic innovation.
RESPONSIBILITIES
Analyze and interpret biomarker data using machine learning (ML) and deep learning (DL) approaches. Develop novel models, including neural networks and graph neural networks, to extract meaningful insights from high-dimensional datasets. Apply deep learning to digitalized scans and imaging data to identify biomarkers and other actionable insights. Explore and implement innovative methodologies, going beyond traditional biomarkers into new areas of data-driven research. Conduct statistical analyses and effectively communicate findings to stakeholders. Collaborate with cross-functional teams across therapeutic areas and all phases of drug development. Leverage external data sources and integrate them into ongoing research projects.
QUALIFICATIONS
MS in statistics, computer science, mathematics or other related disciplines. 3 to 5 years experience in applying complex statistical or AI/ML models, with excellent R/Python programming skills. MS degree or PhD in statistics or related disciplines. Good interpersonal and communication skills. Strong background in machine learning, with expertise in deep learning techniques such as neural networks and image analysis. Hands-on experience with large datasets, including protein expression data and imaging data. Proven ability to develop and implement advanced computational methods, including deep learning frameworks for biomarker discovery. Familiarity with the mechanisms of action of drugs and genomic data is a plus. Excellent skills in statistical analysis and data visualization. Prior experience in biomarkers is an advantage but not essential. Experience in the pharmaceutical industry is not mandatory; a deep understanding of deep learning applications is the primary requirement.
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We are seeking a highly skilled Machine Learning and Deep Learning Scientist to join our team in advancing the development and analysis of biomarkers. This role involves pioneering the use of AI and advanced computational methods to explore large, complex datasets and uncover insights that drive translational medicine and therapeutic innovation.
RESPONSIBILITIES
Analyze and interpret biomarker data using machine learning (ML) and deep learning (DL) approaches. Develop novel models, including neural networks and graph neural networks, to extract meaningful insights from high-dimensional datasets. Apply deep learning to digitalized scans and imaging data to identify biomarkers and other actionable insights. Explore and implement innovative methodologies, going beyond traditional biomarkers into new areas of data-driven research. Conduct statistical analyses and effectively communicate findings to stakeholders. Collaborate with cross-functional teams across therapeutic areas and all phases of drug development. Leverage external data sources and integrate them into ongoing research projects.
QUALIFICATIONS
MS in statistics, computer science, mathematics or other related disciplines. 3 to 5 years experience in applying complex statistical or AI/ML models, with excellent R/Python programming skills. MS degree or PhD in statistics or related disciplines. Good interpersonal and communication skills. Strong background in machine learning, with expertise in deep learning techniques such as neural networks and image analysis. Hands-on experience with large datasets, including protein expression data and imaging data. Proven ability to develop and implement advanced computational methods, including deep learning frameworks for biomarker discovery. Familiarity with the mechanisms of action of drugs and genomic data is a plus. Excellent skills in statistical analysis and data visualization. Prior experience in biomarkers is an advantage but not essential. Experience in the pharmaceutical industry is not mandatory; a deep understanding of deep learning applications is the primary requirement.
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