MD Anderson Cancer Center
Senior Data Scientist - Healthcare AI
MD Anderson Cancer Center, Houston, Texas, United States, 77246
Overview
Senior Data Scientist - Healthcare AI at MD Anderson Cancer Center. Summary: 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 within clinical workflows. Implement robust assurance frameworks to measure and enhance AI solution efficacy, safety, and reliability. Maintain adherence to institutional policies, healthcare regulations, and ethical standards ensuring fairness, transparency, and accountability. Document processes, pipelines, workflows, and ML experiments; communicate results to technical and non-technical audiences. Engage with stakeholders to ensure seamless integration of AI solutions into healthcare systems (e.g., Epic, PACS). Drive innovation by contributing to AI research and industry forums; explore emerging technologies (generative AI, agentic AI) pragmatically. Qualifications & Experience
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/ Doctorate (Academic). Preferred Certifications: Epic certifications; Azure Data Scientist Associate (DP-100); Azure AI Engineer Associate (AI-102); SAFe or equivalent. Certification Required: Must obtain at least one Epic Data Model certification within 180 days of entry into the job. Required Experience: Five years of experience in scientific software or industry programming with concentration in scientific computing; with Master’s degree, three years; with PhD, one year. Preferred Experience/Skills: Two years in Senior Data Scientist role in academia or healthcare; experience with generative AI, LLMs, agentic AI; 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, active learning, scalable data pipelines, MLOps, Python plus C++/C#, TensorFlow, PyTorch, Scikit-learn; healthcare data standards (FHIR, HL7, DICOM); AI lifecycle governance and compliance; first-author publications or equivalent. Analytical Expertise: Ability to translate complex healthcare challenges into structured AI solutions; model lifecycle monitoring, retraining, and risk mitigation; KPI impact evaluation. Oral and Written Communication: Document processes, present to technical/non-technical audiences; Agile project management; stakeholder relationship management. Work Location
Remote in Texas only. Additional Information
Requisition ID: 175350 Employment Status: Full-Time Work Week: Days Minimum Salary: 123,000 USD Midpoint Salary: 154,000 USD Maximum Salary: 185,000 USD FLSA: exempt Work Location: Remote (within Texas only) Referral Bonus Available: Yes Relocation Assistance Available: Yes
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Senior Data Scientist - Healthcare AI at MD Anderson Cancer Center. Summary: 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 within clinical workflows. Implement robust assurance frameworks to measure and enhance AI solution efficacy, safety, and reliability. Maintain adherence to institutional policies, healthcare regulations, and ethical standards ensuring fairness, transparency, and accountability. Document processes, pipelines, workflows, and ML experiments; communicate results to technical and non-technical audiences. Engage with stakeholders to ensure seamless integration of AI solutions into healthcare systems (e.g., Epic, PACS). Drive innovation by contributing to AI research and industry forums; explore emerging technologies (generative AI, agentic AI) pragmatically. Qualifications & Experience
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/ Doctorate (Academic). Preferred Certifications: Epic certifications; Azure Data Scientist Associate (DP-100); Azure AI Engineer Associate (AI-102); SAFe or equivalent. Certification Required: Must obtain at least one Epic Data Model certification within 180 days of entry into the job. Required Experience: Five years of experience in scientific software or industry programming with concentration in scientific computing; with Master’s degree, three years; with PhD, one year. Preferred Experience/Skills: Two years in Senior Data Scientist role in academia or healthcare; experience with generative AI, LLMs, agentic AI; 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, active learning, scalable data pipelines, MLOps, Python plus C++/C#, TensorFlow, PyTorch, Scikit-learn; healthcare data standards (FHIR, HL7, DICOM); AI lifecycle governance and compliance; first-author publications or equivalent. Analytical Expertise: Ability to translate complex healthcare challenges into structured AI solutions; model lifecycle monitoring, retraining, and risk mitigation; KPI impact evaluation. Oral and Written Communication: Document processes, present to technical/non-technical audiences; Agile project management; stakeholder relationship management. Work Location
Remote in Texas only. Additional Information
Requisition ID: 175350 Employment Status: Full-Time Work Week: Days Minimum Salary: 123,000 USD Midpoint Salary: 154,000 USD Maximum Salary: 185,000 USD FLSA: exempt Work Location: Remote (within Texas only) Referral Bonus Available: Yes Relocation Assistance Available: Yes
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