By Aloka Chakravarty, Eli Lilly and Company
For many statisticians early in their careers, the path forward can seem both exciting and uncertain. Should you specialize deeply in one sector or cast a wider net across government, industry, and academia? How do you manage the inevitable growing pains of switching fields? And what does it feel like to rebuild professional expertise from the ground up?
These questions are not abstract. They have shaped my own journey as a statistician working across sectors, and I hope my experiences offer insight for the next generation entering the field.
The Accidental Journey: Not All Careers Are Pre-Planned
One of the most reassuring revelations for early-career statisticians is that even the most impressive career trajectories are rarely the product of a master plan. My decision to join the FDA straight out of graduate school was not purely strategic, but instinctive, driven by the immediate sense of purpose that came from hearing the senior leader’s vision for what the agency could become under the Prescription Drug User Fee Act. The opportunity to contribute to something larger than a single drug or project was simply too compelling to pass up.
Meaningful career decisions are often made at the intersection of opportunity and inspiration, not spreadsheet analysis. For junior statisticians, this means staying genuinely curious and open to opportunities that may not fit neatly into a predetermined plan. The most formative professional experiences frequently arrive unexpectedly.
What Unites All Statistical Roles: The Common Core
Despite differences across sectors at a regulatory agency, a pharmaceutical company, or a university, certain core competencies remain constant. Understanding these shared foundations can help early-career professionals feel more confident about transitioning between sectors.
At the heart of every statistical role is the design, analysis, and interpretation of randomized clinical trials and real-world evidence studies. Whether informing a regulatory submission, supporting a drug development program, or contributing to public health research, statisticians share a common goal: drawing reliable, meaningful conclusions from complex data.
Technical proficiency is equally universal. Familiarity with regulatory frameworks—ICH guidelines, FDA guidance documents, EMA standards—is also valuable regardless of where one sits, because the regulatory dimension shapes how data must be collected, analyzed, and communicated.
In the last decade, real-world data has emerged as a shared frontier. Electronic medical records, insurance claims databases, and patient registries are now central to safety and effectiveness evaluations in both regulatory and commercial contexts. This convergence means statisticians who develop deep expertise in RWD methods in addition to randomized clinical trials will be well-positioned to contribute across institutional boundaries. RWD today sits at a fascinating crossroads of statistics, epidemiology, bioinformatics, and information technology, making it one of the most dynamic and transferable areas of expertise a statistician can cultivate.
What Changes: The Real Challenges of Sector Transitions
Transitioning from a regulatory environment like the FDA into the pharmaceutical industry involves a shift in perspective to an entirely different organizational culture. One of the most striking adjustments was the commercial dimension. In a regulatory agency, the statistician’s primary allegiance is to scientific rigor and public health; in industry, that rigor must coexist with business objectives, funding justifications, and portfolio strategy. Learning to communicate statistical findings in ways that resonate with commercial stakeholders, without compromising scientific integrity, was a skill that took time to hone.
There was also the challenge of shifting from a domestic regulatory mindset to a global perspective. Pharmaceutical companies operate across jurisdictions with varying regulatory expectations, and portfolio strategies often follow tiered approaches designed to satisfy multiple agencies simultaneously. Being accustomed to a single regulatory environment, this scope expansion required intellectual flexibility, and my ICH experience provided the much-needed practical knowledge of international guidelines.
An underappreciated challenge was the skepticism around RWD that persists. Despite growing acceptance by regulators, misconceptions about the appropriate role of RWE—what it can and cannot demonstrate, when it can complement or supplement trial data—remain common. Coming from a regulatory background, I found myself serving as an internal advocate for methodological rigor, helping colleagues understand both the promise and pitfalls of RWD.
Building from the Ground Up: Leading New Teams
My most challenging transition was not just learning a new sector but doing so while simultaneously building a new team or function. This experience, moving into a leadership role in pharma after a regulatory career, showed just how layered the challenge can be. Establishing a team’s foundational framework, cultivating stakeholder relationships, and demonstrating organizational relevance—all while navigating your own personal learning curve—requires a careful balance of humility and confidence. The ability to ask for help, lean on strong managers, and trust in the capabilities of one’s team becomes not just professionally useful, but genuinely essential. For junior statisticians with leadership aspirations, this is a critical lesson: Know when to lead from the front and when to succeed through the people around you.
Practical Advice for Junior Statisticians
For early-career statisticians, several principles stand out. First, know what energizes you. Do you thrive when working deeply on a single product, following it from first-in-human trials to post-market surveillance? Or do you prefer breadth, the landscape-level view that comes from working across a portfolio? Neither preference is better, but knowing which one is yours will help you make smarter decisions about where to invest your energy.
Second, if you’re going to move across sectors, move early. Transitioning earlier in a career preserves more time for growth within a new field. The learning curve is real, but it is also easier to grow when you have fewer entrenched habits and more runway ahead of you.
Third, invest in continuous professional development. The statistical landscape is shifting rapidly. Artificial intelligence and machine learning are no longer peripheral concerns; they are becoming central to how data-driven decisions are made across all sectors. Staying active in professional communities, attending conferences, and engaging with emerging methods is not optional for statisticians who want to remain relevant.
Fourth, embrace the cross-disciplinary nature of the work. The best statistical careers today rarely sit neatly within a single domain. The most impactful work happens at the boundaries where statistics meet clinical sciences and epidemiology, where regulatory science meets commercial strategy, where classical methods meet machine learning.
A Final Word
A career in statistics offers a unique opportunity to contribute meaningfully to quantitative decisions that affect human health and well-being. For junior statisticians navigating that path, the experiences of those who have crossed sector boundaries carry a clear and consistent message: Stay curious, move deliberately, build broadly, and never stop learning.
The crossroads is not something to fear—it is where innovation begins.
Editor’s Note: This article originally ran in the April 2026 issue of Biopharmaceutical Report. It is republished here with permission.
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