Data science plays an increasingly central role in how health care organizations generate evidence, assess risk, and guide decision-making. For the ASA Caucus of Industry Representatives, highlighting leaders who apply statistical rigor and data science responsibly is central to the mission.
ASA Fellow Jing Huang was recently recognized as one of the top women in health care technology for 2025 and received the Council of Chapters ASA Award for Outstanding Chapter Service in 2022.
To celebrate her achievements, ASA Caucus of Industry Representative Rui “Sammi” Tang—global head of quantitative sciences and evidence generation at Astellas Pharma—sat down with her for a conversation about leadership, impact, and the evolving role of data science in health care.
At CareDx, Huang leads enterprise-wide data and AI strategy to advance precision medicine for transplant patients. She is recognized for sustained contributions to statistical innovation, leadership, and service to the profession.
Much of your work sits at the intersection of data science, AI, and health care. What does “impact” mean to you in this context?
For me, impact means data and models actually change decisions and outcomes. In health care, it’s not enough for an algorithm to be technically strong or statistically elegant. It must be trusted, actionable, and integrated into real clinical and operational workflows. True impact happens when data science helps clinicians and organizations make better decisions and when it responsibly improves patient outcomes.
You lead enterprise-wide data and AI strategy in a highly regulated health care environment. What are the biggest challenges in translating advanced analytics into practice?
One of the biggest challenges is bridging the gap between innovation and adoption. Health care systems are complex, and regulation is an essential part of protecting patients. Data scientists need to understand clinical context, regulatory expectations, and organizational realities—not just the data. Leadership is critical in setting standards for rigor, transparency, and governance while still enabling innovation to move forward.
Your career has evolved from hands-on statistical work to broader leadership roles. How did that transition shape your perspective?
Early in my career, I focused deeply on methodology—designing analyses, developing models, and solving specific scientific problems. Over time, I realized the scale of impact increases when you shift from individual contributions to enabling teams and shaping strategy. Leadership requires communicating complex ideas clearly, aligning stakeholders, and making decisions that balance innovation, risk, and long-term value.
You are also deeply involved in professional service and community building. Why is that important to you?
Data science doesn’t advance in isolation. Communities matter—both within organizations and across the profession. Founding and leading DahShu showed me how impactful it can be to create spaces for learning, mentorship, and collaboration. These efforts help develop future leaders and ensure that data science evolves with integrity, openness, and purpose.
As data science and AI become more influential in health care, what responsibilities come with that influence?
With greater influence comes greater responsibility. Data-driven decisions can directly affect people’s lives, especially in health care. Leaders need to think carefully about ethics, bias, transparency, and long-term consequences. Responsible innovation means building systems that are not only effective, but also fair, explainable, and aligned with patient and societal needs.
What advice would you give to statisticians and data scientists who want to grow into leadership roles?
First, build a strong foundation in rigor and scientific thinking—in the generative AI era, this becomes even more essential, as true leadership depends on the ability to question, validate, and think beyond automated outputs. This foundation is the differentiator between those who simply use tools and those who lead with insight and responsibility. Second, invest in communication and collaboration skills. Leadership is about influence, not just expertise. Finally, learn the broader context—business, regulation, and governance—because many of the most important decisions happen at those intersections.
Looking ahead, what excites you most about the future of data science in health care?
What excites me most is that we’re finally at a point where personalized care can move from aspiration to reality. With the rapid advancement of AI, we now have the capability to learn from complex, longitudinal data in ways that were impossible even a few years ago.
Transplant medicine is a powerful example. For each patient, we have rich, multi-dimensional data collected over time—molecular diagnostics, clinical parameters, treatment history, and outcomes. When applied thoughtfully, advanced AI models can integrate these signals to build a dynamic, personalized profile for everyone. This allows us to move beyond population-level guidelines and toward truly tailored care: anticipating risk earlier, adjusting therapy more precisely, and supporting clinicians with actionable insights grounded in that patient’s unique trajectory.
What energizes me as an executive is not just the technology itself, but the opportunity to operationalize it responsibly at scale—embedding AI into clinical workflows in a way that improves outcomes, strengthens decision-making, and ultimately enhances both patient experience and enterprise performance.
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