Komen Graduate Training Program UT MDACC
Senior Data Scientist - Healthcare AI
Komen Graduate Training Program UT MDACC, Houston, Texas, United States, 77246
Overview
The mission of The University of Texas M. D. Anderson Cancer Center is to eliminate cancer in Texas, the nation, and the world through outstanding programs that integrate patient care, research, prevention, and education. Core to the success of our mission is the ability to orchestrate multidimensional data, data analytics, and machine learning to create sustainable impact within a framework of responsible AI. We are building a dynamic team of machine learning engineers and data scientists that can help us consistently and responsibly accelerate the impact of AI across the enterprise, driving long-lasting improvements in cancer care. We are seeking a Senior Data Scientist with strong expertise in designing, developing, validating, and deploying AI solutions using real-world healthcare data. This role will focus on scalable AI/ML systems tailored for oncology and healthcare, leveraging multimodal data sources such as EHRs, imaging, pathology, genomics, and operational data. The ideal candidate will have hands-on experience deploying AI models into production environments with rigorous validation protocols aligned to clinical and operational outcomes. This role combines advanced data science with AI architecture responsibilities, emphasizing robust AI lifecycle management, regulatory compliance, and cross-functional collaboration. While experience in emerging AI fields such as generative AI and agentic AI is a plus, the core focus remains on delivering validated, impactful AI solutions in healthcare settings. Responsibilities
Design, develop, validate, and deploy scalable machine learning models using multimodal healthcare datasets. Execute rigorous validation through pilot studies, silent trials, and performance monitoring against clinical and operational KPIs. Translate clinical complexities into practical AI-driven insights and actionable solutions. Architect scalable, reliable AI/ML pipelines optimized for production and continuous improvement. Manage AI model lifecycles including versioning, retraining, governance, and regulatory compliance (e.g., ISO/IEC standards, FDA, HIPAA). Collaborate closely with multidisciplinary teams—clinicians, data engineers, ML engineers—to integrate AI effectively within clinical workflows. Implement robust assurance frameworks to objectively measure and enhance AI solution efficacy, safety, and reliability. Maintain strict adherence to institutional policies, healthcare regulations, and ethical standards ensuring fairness, transparency, and accountability. Ensure comprehensive documentation to facilitate auditability, transparency, and compliance. Engage stakeholders to ensure seamless integration of AI solutions into healthcare systems (e.g., Epic, PACS). Clearly document workflows, model performance, and communicate results to technical and non-technical audiences. Drive innovation by contributing to AI research and industry forums, positioning the institution as a leader in responsible healthcare AI. Explore emerging technologies (generative AI, agentic AI) pragmatically, identifying viable opportunities for integration. Technical Expertise & Qualifications
Required Education: Bachelor's degree in Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Science, Engineering, Computer Science, Statistics, Computational Biology, or related field. Preferred Education: Master’s or Doctorate Required Experience: Five years of experience in scientific software or industry programming with a concentration in scientific computing. With a Master’s degree, three years; with a PhD, one year. Preferred Experience/Skills: Two years in a Senior Data Scientist role in academia or healthcare, exposure to generative AI, large language models (LLMs), and agentic AI, experience with AI orchestration and autonomous agent development, knowledge of enterprise healthcare systems (EHR/Epic, PACS, ERP). Preferred Technical Experience: Experience with human-in-the-loop systems and active learning for data curation, proven track record building and validating AI models on real-world data, scalable data pipelines, model artifact management, and model performance analytics, experience with MLOps tools, strong Python and either C++ or C#, practical knowledge of TensorFlow, PyTorch, and Scikit-learn, familiarity with healthcare data types and standards (FHIR, HL7, DICOM), strong understanding of AI lifecycle management, governance, and compliance with healthcare regulations, and first-author publications in AI/healthcare domains. Certification: Epic Data Model certification within 180 days of entry; Epic Cognito, Caboodle, and Cognitive Compute certifications are preferred. Azure Data Scientist Associate (DP-100) or equivalent; Azure AI Engineer Associate (AI-102) or equivalent; SAFe or equivalent. Other requirements per policy. Required Experience: Five years in scientific software or industry programming with a concentration in scientific computing. With Master’s, three years; with PhD, one year. Preferred: two years in a Senior Data Scientist role with exposure to generative AI, LLMs, and agentic AI; experience with enterprise healthcare systems. Location & Compliance
Work Location: Remote in Texas only. This position may be responsible for maintaining the security and integrity of critical infrastructure and may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment. EEO Statement: It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. Additional Information Requisition ID: 175350 Employment Status: Full-Time Employee Status: Regular Work Week: Days Salary: $123,000 - $185,000 FLSA: exempt Work Location: Remote (within Texas only) Pivotal Position: Yes Referral Bonus Available: Yes Relocation Assistance Available: Yes
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The mission of The University of Texas M. D. Anderson Cancer Center is to eliminate cancer in Texas, the nation, and the world through outstanding programs that integrate patient care, research, prevention, and education. Core to the success of our mission is the ability to orchestrate multidimensional data, data analytics, and machine learning to create sustainable impact within a framework of responsible AI. We are building a dynamic team of machine learning engineers and data scientists that can help us consistently and responsibly accelerate the impact of AI across the enterprise, driving long-lasting improvements in cancer care. We are seeking a Senior Data Scientist with strong expertise in designing, developing, validating, and deploying AI solutions using real-world healthcare data. This role will focus on scalable AI/ML systems tailored for oncology and healthcare, leveraging multimodal data sources such as EHRs, imaging, pathology, genomics, and operational data. The ideal candidate will have hands-on experience deploying AI models into production environments with rigorous validation protocols aligned to clinical and operational outcomes. This role combines advanced data science with AI architecture responsibilities, emphasizing robust AI lifecycle management, regulatory compliance, and cross-functional collaboration. While experience in emerging AI fields such as generative AI and agentic AI is a plus, the core focus remains on delivering validated, impactful AI solutions in healthcare settings. Responsibilities
Design, develop, validate, and deploy scalable machine learning models using multimodal healthcare datasets. Execute rigorous validation through pilot studies, silent trials, and performance monitoring against clinical and operational KPIs. Translate clinical complexities into practical AI-driven insights and actionable solutions. Architect scalable, reliable AI/ML pipelines optimized for production and continuous improvement. Manage AI model lifecycles including versioning, retraining, governance, and regulatory compliance (e.g., ISO/IEC standards, FDA, HIPAA). Collaborate closely with multidisciplinary teams—clinicians, data engineers, ML engineers—to integrate AI effectively within clinical workflows. Implement robust assurance frameworks to objectively measure and enhance AI solution efficacy, safety, and reliability. Maintain strict adherence to institutional policies, healthcare regulations, and ethical standards ensuring fairness, transparency, and accountability. Ensure comprehensive documentation to facilitate auditability, transparency, and compliance. Engage stakeholders to ensure seamless integration of AI solutions into healthcare systems (e.g., Epic, PACS). Clearly document workflows, model performance, and communicate results to technical and non-technical audiences. Drive innovation by contributing to AI research and industry forums, positioning the institution as a leader in responsible healthcare AI. Explore emerging technologies (generative AI, agentic AI) pragmatically, identifying viable opportunities for integration. Technical Expertise & Qualifications
Required Education: Bachelor's degree in Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Science, Engineering, Computer Science, Statistics, Computational Biology, or related field. Preferred Education: Master’s or Doctorate Required Experience: Five years of experience in scientific software or industry programming with a concentration in scientific computing. With a Master’s degree, three years; with a PhD, one year. Preferred Experience/Skills: Two years in a Senior Data Scientist role in academia or healthcare, exposure to generative AI, large language models (LLMs), and agentic AI, experience with AI orchestration and autonomous agent development, knowledge of enterprise healthcare systems (EHR/Epic, PACS, ERP). Preferred Technical Experience: Experience with human-in-the-loop systems and active learning for data curation, proven track record building and validating AI models on real-world data, scalable data pipelines, model artifact management, and model performance analytics, experience with MLOps tools, strong Python and either C++ or C#, practical knowledge of TensorFlow, PyTorch, and Scikit-learn, familiarity with healthcare data types and standards (FHIR, HL7, DICOM), strong understanding of AI lifecycle management, governance, and compliance with healthcare regulations, and first-author publications in AI/healthcare domains. Certification: Epic Data Model certification within 180 days of entry; Epic Cognito, Caboodle, and Cognitive Compute certifications are preferred. Azure Data Scientist Associate (DP-100) or equivalent; Azure AI Engineer Associate (AI-102) or equivalent; SAFe or equivalent. Other requirements per policy. Required Experience: Five years in scientific software or industry programming with a concentration in scientific computing. With Master’s, three years; with PhD, one year. Preferred: two years in a Senior Data Scientist role with exposure to generative AI, LLMs, and agentic AI; experience with enterprise healthcare systems. Location & Compliance
Work Location: Remote in Texas only. This position may be responsible for maintaining the security and integrity of critical infrastructure and may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment. EEO Statement: It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. Additional Information Requisition ID: 175350 Employment Status: Full-Time Employee Status: Regular Work Week: Days Salary: $123,000 - $185,000 FLSA: exempt Work Location: Remote (within Texas only) Pivotal Position: Yes Referral Bonus Available: Yes Relocation Assistance Available: Yes
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