Senior Data Scientist - Healthcare AI

University of Texas MD Anderson Cancer Center; Houston (TX); Remote; USD 123,000 - 185,000; Full time

Job summary

MD Anderson Cancer Center seeks a Senior Data Scientist focused on AI solutions in healthcare. This role emphasizes the development and deployment of scalable machine learning models within oncology, leveraging diverse data sources. Applicants should possess a strong understanding of healthcare data types and standards, and have hands-on experience in machine learning and AI operations.

Benefits

Referral bonus available

Relocation assistance available

Qualifications

• Experience with ML models and real-world data is essential.
• Hands-on expertise in MLOps tools and processes preferred.
• Strong understanding of healthcare data standards necessary.

Responsibilities

• Design and validate scalable machine learning models using healthcare data.
• Collaborate with cross-functional teams to integrate AI solutions.
• Ensure compliance with healthcare regulations and institutional policies.

Skills

Machine learning algorithms

AI lifecycle management

Data analysis

Collaboration

Communication

Education

Bachelor's degree in a related field

Master's/Doctorate preferred

Tools

Python

TensorFlow

Scikit-learn

PyTorch

Job description

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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.

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.

Core Responsibilities Include

AI Development & Validation

• 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.
  
  

AI Architecture & Lifecycle Management

• 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).
  
  

Integration & AI Assurance

• 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.
  
  

Governance & Compliance

• 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.
  
  

Collaboration & Communication

• Engage effectively with 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 alike.
  
  

Innovation & Thought Leadership

• 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

Hands-on experience and in-depth understanding of machine learning algorithms and modeling (e.g., supervised, unsupervised, semi-supervised or weakly supervised learning, generative models, transfer learning, optimization, etc.).

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, skilled in constructing scalable data pipelines, model artifact management, and model performance analytics, experienced with MLOps tools and processes for data, features, code, and model management, strong proficiency in Python and either C++ or C#, with practical knowledge of TensorFlow, PyTorch, and Scikit-learn, experience with healthcare data types and standards (FHIR, HL7, DICOM), strong understanding of AI lifecycle management, governance, and compliance with healthcare regulations, authorship as first author on peer-reviewed publications or equivalent evidence demonstrating impactful application of AI/ML in healthcare or biomedical domains.

Analytical Expertise

Proficient in translating complex, ambiguous healthcare challenges into structured, data-driven AI solutions.

In-depth knowledge of AI/ML model lifecycle management including ongoing monitoring, retraining, and risk mitigation strategies.

Proficient in decision-making, problem-solving, and executing AI/ML healthcare solutions.

Experience with AI model validation in clinical or operational settings, including designing assurance studies and evaluating impact on relevant KPIs.

Competent in identifying risks and formulating mitigation plans to prevent project delays.

Oral and Written Communication

Collaborate with research data scientists, ML engineers, and software engineers to integrate machine learning models into existing systems.

Document processes, pipelines, workflows, and machine learning experiments.

Skilled in applying project management frameworks (e.g., Agile) to track progress and communicate risks and impacts to a variety of audiences.

Experience presenting complex technical concepts clearly to both technical and non-technical audiences through reports, meetings, and professional forums.

Manage stakeholder relations to facilitate solution adoption and address issues.

Other duties as assigned

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 Certification

Epic certified; EPIC cogito, EPIC caboodle, EPIC Cognitive Compute.

Azure Data Scientist Associate (DP-100) or equivalent.

Azure AI Engineer Associate (AI-102) or equivalent.

SAFe (Scaled Agile Framework) Certifications or equivalent.

Certification Required: Must obtain at least one Epic Data Model certification issued by Epic within 180 days of date of entry of job.

Required Experience: Five years of experience in scientific software or industry programming with a concentration in scientific computing. With Master's degree, three years' experience required. With PhD, one year of experience required.

Preferred Experience/Skills: Two years of academic or healthcare industry in a Senior Data Scientist role, exposure to generative AI, large language models (LLMs), and agentic AI with a desire to grow these skills, experience with AI orchestration and autonomous agent development, knowledge of enterprise healthcare systems (e.g. 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, skilled in constructing scalable data pipelines, model artifact management, and model performance analytics, experienced with MLOps tools and processes for data, features, code, and model management, strong proficiency in Python and either C++ or C#, with practical knowledge of TensorFlow, PyTorch, and Scikit-learn, experience with healthcare data types and standards (FHIR, HL7, DICOM), strong understanding of AI lifecycle management, governance, and compliance with healthcare regulations, authorship as first author on peer-reviewed publications or equivalent evidence demonstrating impactful application of AI/ML in healthcare or biomedical domains.

Work Location: Remote in Texas only.

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. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html

Additional Information

• Requisition ID: 175350
• Employment Status: Full-Time
• Employee Status: Regular
• Work Week: Days
• Minimum Salary: US Dollar (USD) 123,000
• Midpoint Salary: US Dollar (USD) 154,000
• Maximum Salary : US Dollar (USD) 185,000
• FLSA: exempt and not eligible for overtime pay
• Fund Type: Hard
• Work Location: Remote (within Texas only)
• Pivotal Position: Yes
• Referral Bonus Available?: Yes
• Relocation Assistance Available?: Yes
• Science Jobs: No

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Hospitals and Health Care

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