Senior Data Scientist

Senior Data Scientist – Healthcare AIJoin us to apply for the Senior Data Scientist - Healthcare AI role at Komen Graduate Training Program UT MDACC.SummaryThe 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.Core ResponsibilitiesAI Development & ValidationDesign, 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 ManagementArchitect 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 AssuranceCollaborate 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 & ComplianceMaintain 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 & CommunicationEngage 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 LeadershipDrive 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 ExpertiseHands‑on experience and in-depth understanding of machine learning algorithms and modeling (e.g., supervised, unsupervised, semi‑supervised, 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 ExpertiseProficient 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 CommunicationCollaborate 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.Education & ExperienceRequired 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 (Academic).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, experience with AI orchestration and autonomous agent development, knowledge of enterprise healthcare systems (e.g., EHR/Epic, PACS, ERP).Preferred Technical Experience: Duplicate of Technical Expertise listed above for emphasis.CertificationsEpic 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 the job.Work Location & CompensationRemote in Texas only.Minimum Salary: US Dollar (USD) 123,000; Midpoint Salary 154,000; Maximum Salary 185,000.Requisition ID: 175350; Employment Status: Full‑Time; Employee Status: Regular; Work Week: Days; FLSA: exempt; Fund Type: Hard; Referral Bonus Available: Yes; Relocation Assistance Available: Yes.BenefitsThe University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition.Equal Opportunity EmploymentIt 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.Other InformationWork Week: DaysMinimum Salary: US Dollar (USD) 123,000Midpoint Salary: US Dollar (USD) 154,000Maximum Salary : US Dollar (USD) 185,000FLSA: exempt and not eligible for overtime paymentFund Type: HardPivotal Position: YesReferral Bonus Available?: YesRelocation Assistance Available?: YesScience Jobs: No #J-18808-Ljbffr