MD Anderson Cancer Center
Associate Data Scientist Algorithm Research TMI
MD Anderson Cancer Center, Houston, Texas, United States, 77246
Associate Data Scientist Algorithm Research TMIQualifications
Proficiency in Python, complemented by experience with machine learning libraries such as TensorFlow, PyTorch, and Scikit-learnExperience with state-of-the-art machine learning algorithms and training methods for computer visionExperience with essential software engineering principles, including code management, code quality, testing, and debuggingDrive: Analytical ThinkingExperience with maintaining feature management, data labeling, ML artifact management, and analytics to ensure high data quality, versioning, and performance trackingExcellent testing, debugging, and problem-solving skillsExperience with medical imaging data (e.g., DICOM)Ability to assist researchers to gather initial requirements, analyze clinical data, design and develop ML solutions, perform feasibility testing of proposed solutions, evaluate and interpret the resultsEducation Required: Bachelor's degree in Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Statistics, Computer Science, Computational Biology, or related fieldExperience Required: Two years' experience in scientific software development/analysisTwo years of academic or industry experience working on AI computer vision techniques applied to healthcareAt least two publications relating to machine learning applied to healthcareOne year of professional software development experience working within academia or industryExperience working with large DICOM datasetsExperience with cloud-native tools for machine learning in Azure, AWS, or GCPResponsibilities
The successful candidate will collaborate closely with our team of research data scientists, using deep learning methodologies to create impactful AI algorithmsThe candidate will maintain diligent records of model development experiments and create comprehensive model scorecardsThey will also be responsible for conducting a thorough evaluation of machine learning (ML) model performance and analyzing bias and equity to ensure our commitment to responsible AITrack record of developing and deploying impactful computer vision models for segmenting and interpreting complex medical imaging dataProfessionalism: Oral and Written CommunicationAbility to communicate effectively and cooperatively with leaders, peers, end users, and support teams when requiredOther duties as assignedBenefits
Midpoint Salary: US Dollar (USD) 94,000Maximum Salary: US Dollar (USD) 113,000FLSA: exempt and not eligible for overtime payFund Type: SoftThe University of Texas MD Anderson Cancer Center is devoted to improving the care of cancer patients by optimizing multidimensional data integration, analysis, and application. This commitment is brought to life in the Institute for Data Science in Oncology (IDSO), a flagship program designed to reshape patient experiences and fast-track scientific breakthroughs via data-driven cancer care. The IDSO embodies a data-centric culture, pushing the envelope of modern care and venturing into uncharted territories of cancer research and treatment.
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Proficiency in Python, complemented by experience with machine learning libraries such as TensorFlow, PyTorch, and Scikit-learnExperience with state-of-the-art machine learning algorithms and training methods for computer visionExperience with essential software engineering principles, including code management, code quality, testing, and debuggingDrive: Analytical ThinkingExperience with maintaining feature management, data labeling, ML artifact management, and analytics to ensure high data quality, versioning, and performance trackingExcellent testing, debugging, and problem-solving skillsExperience with medical imaging data (e.g., DICOM)Ability to assist researchers to gather initial requirements, analyze clinical data, design and develop ML solutions, perform feasibility testing of proposed solutions, evaluate and interpret the resultsEducation Required: Bachelor's degree in Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Statistics, Computer Science, Computational Biology, or related fieldExperience Required: Two years' experience in scientific software development/analysisTwo years of academic or industry experience working on AI computer vision techniques applied to healthcareAt least two publications relating to machine learning applied to healthcareOne year of professional software development experience working within academia or industryExperience working with large DICOM datasetsExperience with cloud-native tools for machine learning in Azure, AWS, or GCPResponsibilities
The successful candidate will collaborate closely with our team of research data scientists, using deep learning methodologies to create impactful AI algorithmsThe candidate will maintain diligent records of model development experiments and create comprehensive model scorecardsThey will also be responsible for conducting a thorough evaluation of machine learning (ML) model performance and analyzing bias and equity to ensure our commitment to responsible AITrack record of developing and deploying impactful computer vision models for segmenting and interpreting complex medical imaging dataProfessionalism: Oral and Written CommunicationAbility to communicate effectively and cooperatively with leaders, peers, end users, and support teams when requiredOther duties as assignedBenefits
Midpoint Salary: US Dollar (USD) 94,000Maximum Salary: US Dollar (USD) 113,000FLSA: exempt and not eligible for overtime payFund Type: SoftThe University of Texas MD Anderson Cancer Center is devoted to improving the care of cancer patients by optimizing multidimensional data integration, analysis, and application. This commitment is brought to life in the Institute for Data Science in Oncology (IDSO), a flagship program designed to reshape patient experiences and fast-track scientific breakthroughs via data-driven cancer care. The IDSO embodies a data-centric culture, pushing the envelope of modern care and venturing into uncharted territories of cancer research and treatment.
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