Lead Data Scientist

We're seeking a Data Scientist who thrives in a fast-paced, collaborative environment. In this role, you will lead the design, development, and deployment of advanced analytics, machine learning, and AI solutions using one of the nation's largest pediatric healthcare datasets. Partnering with clinical, operational, and technical leaders, you will drive data-driven innovation that improves patient outcomes, enhances hospital operations, and advances Texas Children's mission to create healthier futures for children worldwide.

Think you’ve got what it takes?

Job Duties & Responsibilities:
•    Analyze large, complex, and noisy structured and unstructured datasets to uncover meaningful patterns, generate actionable insights, and drive data-informed decision-making.
•    Establish data quality standards and lead data preparation efforts, including profiling, validation, cleansing, and feature engineering activities.
•    Design, develop, validate, and optimize advanced statistical, optimization, machine learning, and AI models, including regression, classification, clustering, natural language processing, deep learning, and generative AI applications.
•    Analyze large, complex, and noisy structured and unstructured datasets to uncover meaningful patterns, generate actionable insights, and drive data-informed decision-making.
•    Lead the development of descriptive, diagnostic, predictive, and prescriptive analytics solutions, partnering with data architects, engineers, clinicians, and business stakeholders to ensure scalable and impactful outcomes.
•    Architect and operationalize predictive models and advanced analytical solutions that address critical clinical, operational, and strategic business challenges.
•    Provide end-to-end ownership of data science initiatives, from problem definition and stakeholder alignment through model deployment, monitoring, maintenance, and continuous improvement.
•    Collaborate with clinical, operational, and executive stakeholders to identify opportunities where data science, AI, and machine learning can improve patient outcomes, operational efficiency, and organizational performance.
•    Translate complex analytical findings into compelling narratives and executive-level presentations that drive understanding, alignment, and action among non-technical audiences.
•    Develop intuitive and impactful visualizations that effectively communicate complex relationships, trends, and insights to diverse stakeholder groups.
•    Serve as a trusted advisor and subject matter expert, providing statistical, analytical, and AI guidance to project teams, leadership, architects, and developers.
•    Apply domain expertise to guide analytical approaches, feature selection, model design, and evaluation strategies while ensuring alignment with business and clinical objectives.
•    Mentor and coach data scientists and analysts, promoting best practices in model development, experimentation, reproducibility, and responsible AI.
•    Lead model governance activities, including performance monitoring, drift detection, validation, explainability, and compliance with organizational AI policies and standards.
•    Estimate effort, manage priorities, communicate project status, and proactively identify and mitigate risks throughout the project lifecycle.
•    Champion Agile practices and contributes to strategic planning, iteration execution, backlog refinement, retrospectives, and continuous process improvement initiatives.
•    Evaluate and incorporate emerging technologies, advanced analytics techniques, and AI innovations to enhance organizational capabilities and accelerate value delivery.
 

Skills & Requirements:
•    Master's degree in data science, mathematics, computer science, statistics, engineering, analytics, life sciences, economics, finance, business or a specific healthcare field (such as nursing, pharmacy, respiratory, etc.) required
•    Ph.D. preferred
•    5 years' experience in data science, data analytics, business analysis, data modeling, statistical analysis, statistical modeling, or machine learning, including 2 years healthcare industry experience required
•    A PhD may substitute for two years of experience.
•    Two years of licensed, clinical healthcare experience may substitute for 2 years of experience