What does it mean to be a statistician, and how do you get there? In the April issue of Amstat News, ASA President Jeri Mulrow reflected on this question. We’re sharing her insights here for students or anyone considering a future in statistics.
Mathematics and Statistics Awareness Month exists to remind the world that statistics matters. Most of us reading this don’t need that reminder. We might benefit from a different one: Being a statistician is a professional identity worth claiming deliberately, especially in a job market in which our skills are in demand but our title may be invisible. The question, “What is a statistician?” turns out to have a surprisingly useful answer when you approach it from the direction of a job search.
In the span of a decade, “data” has gone from a professional specialty to a cultural obsession. At some level, everyone is doing data science now. Journalists, policymakers, engineers, marketers, coaches, and, in fact, every citizen talks about data fluency as essential. And yet, in a world in which everyone claims to work with data, the statistician stands apart. Not because we are the only ones running models—we are not—but because we bring to those models a disciplined framework for reasoning under uncertainty, a commitment to understanding how data came to be, and a willingness to say “we don’t know” when the evidence doesn’t support a confident conclusion. This is part of our professional identity. This is a good moment to be proud of that identity and support deliberate investment in it, especially for those who may be deciding on an educational path or making a career transition.
For students weighing graduate programs in statistics or data science, the array of options can feel overwhelming. The landscape is rich with opportunities in statistics and biostatistics departments, interdisciplinary data science programs, computational science tracks, and more. I would like to share advice I would now give to my younger self. First, look for programs in which faculty are actively engaged with problems that ignite your curiosity. Statistical education happens at the intersection of rigorous methods and genuine scientific questions. Second, pay attention to the breadth of the curriculum, so it will provide options for you as your awareness and interests grow. Third, participate in ASA events and talk to current students and recent graduates. Their experience of the culture, advising, and job placement will tell you more than a program’s website ever will. Also resist the pressure to optimize narrowly. A program in which you will have strong mentorship, collaborative peers, and opportunities to work on real problems will serve you better in the long run. Our field is relationship-driven; your adviser and cohort will be critical in shaping your career.
If there is one piece of career advice I would offer without qualification, it is this: Do the internship. If you can, do more than one and explore different areas. The transition from academic training to professional practice is not automatic, and internships are where that translation happens. Working in a government agency, a pharmaceutical company, a tech firm, a hospital, or a nonprofit will help you learn how to scope an ambiguous problem, communicate findings to a nontechnical audience on a deadline, and function in a team in which you are not the only expert in the room. These skills are as important to a successful statistical career as any theorem you will learn in a classroom.
Internship experiences also help you discover which sectors genuinely excite you. Many students arrive in graduate school with a vague sense that they want to “work in industry” or “go into academia” without having tested those assumptions. An internship can confirm your direction or redirect it entirely, and either outcome is valuable.
The job search in statistics and data science is genuinely different from what many other fields experience. Positions are spread across federal agencies, academic research centers, hospitals and health systems, technology companies, consulting firms, financial institutions, and nonprofits, and the titles vary so widely that “statistician,” “data scientist,” “quantitative analyst,” “research scientist,” and “biostatistician” can describe nearly identical work in different organizations. Cast a wide net when searching, and don’t anchor too narrowly on job titles.
A cover letter for a statistical role is successful if it connects your specific training and experience to the actual problem the employer is trying to solve. Avoid the temptation to summarize your résumé in paragraph form. Instead, identify one or two aspects of the role or organization that genuinely interest you and make a specific case for why your background prepares you to contribute. If the position involves survey methodology, say something about your experience with survey design. If it’s a clinical trials role, name the relevant coursework, internship, or research. Specificity signals both competence and genuine interest, which are exactly what hiring managers are looking for.
In 2026, it is almost a requirement to have a current LinkedIn profile, but don’t stop there. A GitHub profile with well-documented projects gives employers something concrete to evaluate beyond your résumé. If you’ve contributed to open-source packages, presented at a conference, or published any kind of written work, link to it. In a competitive job market, a polished online presence is no longer optional. Your profile should tell a coherent story and document what problems you work on, what tools you use, and what you care about professionally.
Statistical interviews vary considerably by sector. In industry and tech, you should expect technical screening questions. You may be asked to complete coding exercises in R or Python or address case-style questions where you are asked to design an analysis or interpret a result. In government and academic research settings, interviews tend to focus more on your project experience, your ability to communicate methods to nonstatisticians, and how you approach ambiguous problems. Prepare for both. Practice explaining your past work out loud. This is not just a description of what you did, but why you made the methodological choices you did and what you would do differently now. The ability to describe your own reasoning is one of the most underrated interview skills in this field.
Before you apply broadly, invest time in conversations with people doing work you find interesting. Something that distinguishes statisticians from the broader data community is that we have a home. Our community is genuinely willing to spend 20–30 minutes talking with a colleague who reaches out thoughtfully. Ask about how they found their path, what they wish they had known earlier, and what skills they find most valuable in the people they hire or mentor. These conversations will sharpen your sense of direction, often surface opportunities that aren’t publicly posted, and build the network that will carry your career forward.
The American Statistical Association offers some of the best return on engagement in the field. Joining a section that matches your substantive interests, whether that is Bayesian statistics, environmental applications, health policy, social statistics, or sports analytics, connects you immediately with a community of people working on problems you care about.
The ASA’s local chapters provide year-round community. Chapter events are often the most accessible entry point for students and early-career professionals, and they are where many career-defining conversations and mentoring relationships quietly begin.
I want to close by saying it is a remarkable time to be entering this profession. The demand for rigorous statistical thinking has never been higher. The tools have never been more powerful. And the questions have never been more consequential. In a landscape crowded with people claiming statistical authority, the profession’s credibility rests on the quality and integrity of the work ASA members do. It rests on being honest about uncertainty, transparent about methods, and thoughtful about the gap between what data can and cannot tell us. As we celebrate Mathematics and Statistics Awareness Month and begin, continue, or conclude our career journeys, we can also celebrate the profession’s genuine identity, history, and stake in how the world makes decisions.
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