There are many routes a statistician can take to reach an area to study. In an effort to get to know these routes, we asked a few ASA members to answer questions about the paths they took to get where they are today. Their answers are below.

Ryan Janicki

Education: PhD, University of Maryland, College Park
Current position: Mathematical statistician, working with the small area estimation group in the Center for Statistical Research and Methodology at the U. S. Census Bureau

What do you do on a daily basis?

My typical day is probably very similar to that of most statisticians—a mixture of reading and writing papers, analyzing data, and building models.

Name a few specific skills you need to do your job.

Knowledge of survey sampling, missing data methods, and mixed effects models. Most of the computational work I do is with R.

What is a skill you would like to learn to be better at your job?

Really, I’d love to learn more about everything, but I am spending time right now learning more about missing data methods.

What are the most satisfying and most frustrating parts of your job?

The most satisfying part of my job is having the opportunity to work on important applied problems with very talented colleagues, while also having some freedom to explore independent research ideas, usually motivated by Census Bureau problems.

Research, in general, can at times be frustrating, in that the problems we work on rarely have easy solutions and much time can be spent and many ideas discarded before a working solution can be found.

What or who inspired you to become a statistician?

I was a math major in college because it was my favorite subject, but I didn’t know what opportunities there were for mathematicians outside of academia. In graduate school, I wanted to continue to study some theoretical mathematics, but I also wanted to work on applied problems in the future. I felt that studying statistics would be a great way to achieve this balance.

Did you have a mentor?

My advisor in graduate school was Professor Abram Kagan, who suggested many interesting problems to work on and taught me a great deal about mathematics, statistics, and research in general. Also Don Malec, who introduced me to small area estimation and worked with me on my first few projects at the Census Bureau.

What advice would you give to young statisticians just beginning their careers?

I have always found that I learn best by working on challenging problems. My advice would be to explore different areas of statistics and collaborate as much as possible with more experienced statisticians. I also think it is useful to learn a statistical programming language such as R or SAS as early as possible, since nearly all the problems we work on require some coding.

What advice would you give to a student who wants to have the same career path as you?

I think, beyond a general interest in statistics, the most important things for a student who wants to be a statistician in the federal government to have are motivation, curiosity, and an openness to new and interesting problems. Having a specific interest in survey sampling would be helpful, as well.

I think internships can be great learning opportunities. If I were a student, I would seek out as many internship opportunities as I could find, rather than focusing on a single type of internship. I think each work experience is an opportunity to get a feel for what work is done in a particular field and to learn what you do and don’t like within an industry.

Where do you see the future of statistics going? What trends/challenges do you see in the future?

I often hear that statisticians in the federal agencies will continue to be asked to do more with less—to provide more accurate and timely estimates while working with smaller budgets, smaller sample sizes, and higher nonresponse rates. I think it is possible that there will be more model-based methods, such as small area methods, used by survey statisticians in the future.

How has the field of statistics changed since you first started your career?

We have seen an explosion in the size and nature of the data sets we have to analyze and understand. Advances in high-dimensional data and machine learning are making it possible for us to draw meaningful inferences from very complex data.

In what ways have you benefitted from having an ASA membership?

I became a member in 2008. I enjoy reading Amstat News and Significance and attending the Joint Statistical Meetings. The job postings were also very helpful when I was nearing graduation.

If you were not a statistician, you would be …?

A furniture maker.

What do you enjoy doing in your spare time?

Reading, listening to music, woodworking, playing soccer with my kids.

Name your favorite blogs or books you have read and would recommend to others.

Andrew Gelman’s blog, “Statistical Modeling, Causal Inference, and Social Science,” and Nathan Yau’s blog, “FlowingData.com.”

What were you doing at age 28?

Struggling through my fourth year in graduate school.

In 10 years you hope to be doing …?

I very much like what I am doing now and would like to continue being a research statistician for the next 10 years.

Hilary Parker

Education: BA, 2008, mathematics and molecular biology, Pomona College; PhD, 2013, Johns Hopkins Bloomberg School of Public Health
Current position: Data analyst at Etsy
Twitter: @hspter
Blog: hilaryparker.com

What do you do on a daily basis?

I provide analyst support for the buyer experience teams at Etsy. What this means is that I consult with various teams that build features on the website, such as the team that builds the search experience on the website. I help with anything from opportunity sizing to behavioral analysis to formal experiment analysis. I also help build the in-house tooling we have for analyzing experiment results and teach my coworkers about statistics.

Name a few specific skills you need to do your job.

Being an analyst is very much like being a statistical consultant—you need to have a diverse set of skills! You, of course, need general statistical knowledge and the ability to perform good statistical analyses. But beyond that, you need to be able to communicate those results to a diverse audience, whether it’s team members with different skill sets or board members at a board meeting.

Additionally—and just like a good statistical consultant—the more you are embedded into a team, the more you can help lead decisionmaking, rather than simply doing analyses that others ask of you. This means being able to think critically about where there is opportunity, what projects are worth tackling, and what projects will be derailing for little benefit. One of the biggest skills I feel like I have learned is the ability to say, “I know there is bias in this analysis, but I also know that bias is bounded and my decision wouldn’t change if I measured it.” Having that ability to know where to invest your energy is huge.

What is a skill you would like to learn to be better at your job?

Software engineering, without a doubt. Etsy is a really special tech company in that, despite its large size, I can still work with teams across the company to build features our buyers and sellers use, or that we use internally. As I become a better software engineer, I can contribute more directly to that and even push code to production! Additionally, seeing software engineers work (and seeing the code they produce) has tremendously improved my R programming.

What are the most satisfying and most frustrating parts of your job?

I love working with teams—it’s one of the big reasons why I chose working in tech and at Etsy. I’m the only person at Etsy with formal statistical training (although many people are very good at statistics). As a result, I am often able to provide a perspective that others don’t have. The ability to see my training implemented so quickly is hugely rewarding!

Did you have a mentor?

One of my statistics professors at Pomona was Jo Hardin. She and I have stayed in touch ever since. One time when we were at a conference together and I was updating her about my life, she told me, “I can’t tell you how many times that I thought I had made a decision for forever.”

As my career has twisted and turned in ways I didn’t expect, that advice has really stuck with me. I think a lot of folks—and probably especially statisticians—have a desire to map and plan everything out, so it can be hard if those plans don’t turn out. Learning to embrace that uncertainty has helped me build a career I really love.

What advice would you give to other young statisticians just beginning their careers?

Don’t be afraid to keep learning! Being a good applied statistician means keeping up with statistical theory, statistical programming languages and software, and whatever field you’re analyzing. One of the most energizing things about statistics (that can also be exhausting) is you can’t rest on your laurels!

What advice would you give to a student who wants to have the same career path as you?

If you are thinking about a job in tech, I highly recommend seeking out a summer internship while you’re in school. It can be hard for a company to hire a statistician with a purely academic track record, since a job in tech requires domain knowledge that’s not necessarily taught in graduate school. Since I didn’t have internship experience, I know Etsy had to take a leap of faith on me that I would be able to pick up some of the technical skills and business savvy that’s required of an analyst. Having internship experience can take away a lot of that uncertainty.

Where do you see the future of statistics going? What trends/challenges do you see in the future?

It’s really exciting to see other fields such as computational sociology and digital history pop up as the availability of data explodes. Statisticians did a great job of developing methods relevant to medical fields, and I think we need to strive to do the same for these new and exciting subfields.

Additionally, it seems like every major news outlet is developing a data visualization team these days, presumably because data stories are hugely successful. I know that The New York Times dialect map—built on a project Josh Katz performed during his statistics master’s—was their most popular post of 2013 (and it was posted in December). Statistics training has always been about building a narrative and telling a scientific story through data, but the audience is broadening and we need to keep up!

You recently earned your PhD in biostatistics. Congrats! What advice would you give to students who are currently thinking about earning a PhD?

I’d give the same advice that Jo Hardin gave me: “I can’t tell you how often I thought I had made a decision for forever.” If you are excited about getting a statistics PhD, you should do it! And then if you decide to take a path that isn’t traditional, you should do that too! Statistics is great because it lays a foundation on which you can build so many careers.

What do you see as the biggest challenges new graduates face today? What can universities do to better prepare new grads to enter the work force?

I think one of the biggest challenges we face as a field is training our students to have the programming skills required for many modern statistical applications. Many training programs don’t include computer programming and that is a mistake. While Big Data might be a buzzword, it’s also true that the volume of data produced in both science and industry is only going to continue to grow. Being able to deal with this volume of data requires programming savvy that we, as statisticians, aren’t yet providing in our training programs.

In what ways have you benefitted from having an ASA membership?

I became an ASA member during my first year of graduate school and have been to JSM every year since. Especially now that I am outside of academia, I really value being able to gather with other statisticians once a year, compare notes about our work, and bounce ideas off one another. Last year alone, I left the conference with several new ideas for what to implement at Etsy.

If you were not a statistician, you would be …?

I have no idea! It was hard enough figuring out I should be a statistician! ☺

What do you enjoy doing in your spare time?

In graduate school, I played Roller Derby, which was a ton of fun! My derby name was “Status: Bitchin'” and my number was 0.05. Now, however, I find my free time mostly filled with exploring New York City and establishing my life here.

What are one or two favorite blogs or books you have read and would recommend to others?

I have to pitch my advisor’s blog, simplystatistics.org. It’s one of my favorite resources for statistics news and pontifications.

What were you doing at age 28?

I was just starting my job at Etsy! I started my job at Etsy about 3 weeks after I turned 28, which was about 4 weeks after I defended.

In 10 years, you hope to be doing …?

I’m honestly not sure! I have really enjoyed exploring tech and feel like it’s a good fit. But part of the reason I decided not to pursue a career in academia is that I wanted the flexibility to change careers or explore different avenues without restarting the tenure clock. As I’ve worked more and more, I’ve found different strengths and interests emerging that I wouldn’t have found in a more traditional academic route. I’m not sure which one will win out!

Tell us a fun fact about yourself.

My cat is named after a character from “The Wire” because I got her in Baltimore. After I named her, I met the actress who played the character and told her about my cat!

Terrance Savitsky

Education: BSME (mechanical engineering) – Rice University; MBA – The University of Texas at Austin; MILR (human resources) – Cornell University; MA (statistics) – Rice University; PhD (statistics) – Rice University
Current position: Research mathematical statistician in the Office of Survey Methods Research in the Bureau of Labor Statistics

What do you do on a daily basis?

I perform applied methods–based research loosely organized around resolving estimation and inferential issues associated with dependent sample data collected from surveys. Much of my work is motivated by problems that arise from one of the BLS survey programs (e.g. Local Area Unemployment Survey). My projects generally focus on two types of estimation:

• Develop flexible (nonparametric) models to allow the borrowing of information temporally, spatially, and based on other information to provide more stable estimates for small domains (e.g., counties in a state). These approaches input model-free (direct) estimates that account for informative sampling designs. The direct estimates, however, are often unstable for small domains (where the sample size is small).
• Adapt estimation methods to work with the “micro,” respondent-level data in a manner that accounts for the informative sampling design to produce unbiased estimates.

My methods focus is on Bayesian nonparametrics, and I’m working to map this background to the field of survey statistics.

Name a few specific skills you need to do your job.

Statisticians at BLS express a diversity of focus areas. That said, the use of Bayesian methods, which is my focus, is quite new in this environment so there are many challenging opportunities to innovate. Bayesian methods are particularly well suited to estimation of complex dependence relationships that arise from structured data. Data acquired from surveys often express a lot of structure, both induced by the sampling design and also by nature of the survey questions and respondents.

The field of survey sampling, however, is wary of model-based approaches so to not insert unverifiable assumptions (the model) into the production of estimators designed for use by the broader community of researchers and government officials. Yet, flexible nonparametric models often enhance inferential value and provide more usable estimators. Summarizing, from a technical perspective, I need to learn and leverage understanding of Bayesian methods to analyze data collected from informative sampling designs.

I regularly dialogue with nonstatistician researchers and practitioners where it is important to tailor the presentation of one’s work to the needs and interests of each of those audiences. For example, I recently worked on a project to estimate sets of time-indexed functions that express dependence across observations for a BLS program that produces unemployment estimates for local areas. They were less interested in the technical aspects of the method I employed and more interested in the estimation properties and inference. So I focused communicating the features of the formulation by using results across data sets that each expressed distinct challenges that would highlight model estimation properties in an intuitive way.

Writing well is, of course, important for both composing research papers and communicating ideas to practitioners who manage BLS programs.

What is a skill you would like to learn to be better at your job?

I’d like to be a better-skilled research statistician. I need more arrows in my quiver. The arrows include leveraging applied probability concepts in my research and developing computationally efficient estimation methods. On the latter point, data acquired from surveys is generally of very high quality because of the active participation of respondents. Yet while these data are generally not classified as “big,” they are often still quite large.

My implementations of Bayesian nonparametric models have always sampled the marginal distributions over the set of model parameters, which allows for very straightforward inference. Sampling distributions is, however, computationally expensive and often doesn’t scale well in sample size (since it is common for parameters to be indexed by observation unit). I quite like the inferential benefits associated with sampling the posterior distribution (as opposed to extracting point estimates under an approximation to the true posterior), but there is the need to develop new approaches. For example, I recently adapted an approach of Radford Neal that produces samples from the exact posterior distribution in a more computationally tractable way by ‘stepping into’ a lower-dimensional temporary space in which samples are moved before ‘stepping out’ to the full-dimensional space.

What are the most satisfying and most frustrating parts of your job?

The most satisfying parts of my work are publishing research papers and producing useful results for BLS program teams. On the latter point, when I deliver a result that a BLS program finds useful, there is much appreciation and that feels great. Whatever frustrations arise are dominated by the appreciation I have for the opportunity to participate in performing research.

What or who inspired you to become a statistician?

I took a sequence of statistics courses while studying for a graduate degree in human resources at Cornell University. The skillful teaching stoked my interest in statistics as a means to more scientifically contribute to public policy decisions. Statistics is a field that ‘parameterizes’ models of human behavior in a fashion that allows the linking of art and science. The juxtaposition of qualitative and quantitative, conceptual and cause/effect, allowed by the field of statistics inspires me.

Did you have a mentor?

Besides my thesis advisor, Marina Vannucci, I’ve received patient and thoughtful advice from Katherine Ensor, Peter Mueller, and Matt Schonlau. The advice I’ve most taken to heart is to express persistence. Statistics is a difficult field, so it’s important to pursue one’s work with enthusiasm and focus to occasionally achieve a good result.

What advice would you give to young statisticians just beginning their careers?

Given my indirect path to the field of statistics, I would suggest upcoming statisticians to consider that they are navigating a nonlinear journey, rather than moving through a pre-planned set of steps, and to take risks where their interests are piqued.

What advice would you give to a student who wants to have the same career path as you?

Focusing on building research methodology skills—including formulation, analysis, and writing—is probably the best preparation. Taking the opportunity to present one’s work also provides excellent preparation for communicating with a diverse audience.

Where do you see the future of statistics going?

Everyone will be Bayesian. Maybe not, but it was fun to write that. Here at BLS, I think there will be a growing interest in approaches that offer useful inference about the nature of dependence. There’s been a traditional focus on producing increasingly more efficient estimators and that will continue, though through modeling rather than closed-form approaches. The modeling approaches that will be most readily accepted are those that offer inferential insight to frame the results they produce. I have no idea how the broader field will evolve, but I do know we will never have enough computing power so we will always require clever probabilistic formulations and implementation algorithms.

How has the field of statistics changed since you first started your career?

Machine learning approaches and the convergence in some topics among the fields of computer science, engineering, and statistics are probably the biggest ongoing change. Probabilistic underpinnings to support the adaptation of nonparametric modeling also continue apace.

In what ways have you benefitted from having an ASA membership?

I became an ASA member during my graduate studies (in 2008) and have benefited from the exposure to the community of researchers that conferences and workshops sponsored by the ASA offer. It is through very proactive organizations like the ASA that we become more than individual researchers and operate as a community.

If you were not a statistician, you would be …?

Unemployed. Seriously, I’d probably be a behavioral therapist as there’s nothing that interests me more than people’s archetypal stories.

What do you enjoy doing in your spare time?

I run. A lot. Every day.

Name your favorite blogs or books you have read and would recommend to others.

I’m afraid to admit that since entering the field of statistics, nearly all of my long-form reading relates to the field. I follow Andrew Gelman’s blog (and BDA3), enjoyed reading Dirk Eddelbuettel’s Rcpp book, and I like anything written by Hadley Wickham and David Dunson. Research papers are generally more important for me than books. There are just too many beautifully written papers to select from for comment.

What were you doing at age 28?

I was developing the concept for a future sport utility vehicle in the auto industry and playing a lot of ultimate Frisbee in my free time.

In 10 years, you hope to be doing …?

I hope to be a skillful and effective statistical researcher in 10–20 years.

What is your Twitter handle or personal blog?

I still haven’t switched from an ordinary cell phone to a smart phone.

Tell us a fun fact about yourself.

I’ve participated in six marathons and don’t use running shoes.

Christopher Schmid

Education: BA, mathematics, Haverford College, 1983; PhD, statistics, Harvard University, 1991
Current position: Professor of biostatistics, Brown University

What do you do on a daily basis?

Answer email, teach, advise and mentor students, write and edit manuscripts and protocols, administer a graduate program, serve on university and professional and government committees, provide advice to biomedical colleagues, analyze and supervise analysis of data, research new problems, and program new methods.

Name a few specific skills you need to do your job.

Writing and editing, oral communication and speaking in front of an audience, computer programming, mathematics, and knowledge of the world around me and how it relates to the problems I work on.

What is a skill you would like to learn to be better at your job?

How to use many of the new social networking tools more effectively.

What are the most satisfying and most frustrating parts of your job?

Rewarding: Helping others to solve their problems and learn new things.
Frustrating: Lack of time to learn new statistical techniques. After awhile, much of your term is spent advising and supervising and the time to learn new things shrinks.

What or who inspired you to become a statistician?

I was always interested in sports and statistics as a kid and spent two years as an actuary after college. I liked the statistics, but not the business, so went to get my PhD.

Did you have a mentor?

I learned from a variety of both statisticians and doctors about different aspects of biostatistics.

What advice would you give to young statisticians just beginning their careers?

Learn how to communicate effectively as a writer, speaker, and conversationalist.

What advice would you give to a student who wants to have the same career path as you?

Do as much applied work as you can and get into an environment where you can participate in planning and discussion. Learn to communicate and how to interact with scientists who are not statisticians. Learn how to consult and find mentors/bosses who will help you learn how to be most effective.

Where do you see the future of statistics going?

As the amount of data available increases and the need for prediction grows, statisticians will need to deal with more and more data in many dimensions. Computer scientists and others are now developing many computational algorithms that handle masses of data. Statisticians have a key role to play in drawing correct inferences and estimating uncertainty in these predictions, but need to be able to make their points effectively, hence communicate.

How has the field of statistics changed since you first started your career?

The speed of communication is so much faster with the invention of the World Wide Web and the spread of email, blogs, and social networking. The number of people worldwide doing statistics has also increased exponentially. It is a much more competitive world. Also, the power of computation is so much greater now so many new methods that were not possible 25 years ago are now routine. It is much more difficult to keep up, and to be an expert in many branches of statistics is impossible. The discipline is much more specialized now.

In what ways have you benefitted from having an ASA membership?

I joined the ASA in 1986, during my first year of graduate school. The ASA has connected me to a lot of other statisticians, has promoted my research career, and has led me to many opportunities to promote statistics and serve professionally in many realms.

If you were not a statistician, you would be …?

A historian.

What do you enjoy doing in your spare time?

I play soccer, garden, read, travel, and do puzzles.

Name your favorite blogs or books you have read and would recommend to others.

The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged… by Sharon Bertsch McGrayne and The Emperor of All Maladies: A Biography of Cancer by Siddhartha Mukherjee

What were you doing at age 28?

I was in graduate school working on my dissertation, working at a software company training users, and raising two kids.

In 10 years, you hope to be doing …?

What I am doing now: teaching and research and traveling.

Tell us a fun fact about yourself.

I coached youth soccer for 11 years.

Rick Wicklin

Education: BS, mathematics and physics, Guilford College; PhD, applied mathematics, Cornell University
Current position: Distinguished researcher in computational statistics at SAS. I am part of the R&D team that supports and adds new features to SAS software.
Twitter: @RickWicklin
Blog: blogs.sas.com/content/iml

What do you do on a daily basis?

I write software that enable SAS customers to analyze data efficiently and effectively. My main focus is extending and promoting the SAS interactive matrix language (SAS/IML).

Name a few specific skills you need to do your job.

Knowledge of matrix computations, numerical analysis, and multivariate statistics. A solid mastery of computer programming. The ability to communicate orally and in writing with other researchers, with executives, and with sales and marketing staff.

What is a skill you would like to learn to be better at your job?

To multitask more efficiently.

What are the most satisfying and most frustrating parts of your job?

I get a lot of satisfaction from blogging about issues in statistical programming. The most frustrating aspect of my job is deciding which features to add for the upcoming release of SAS software and which features to delay until a future release. I wish there were more hours in the day!

What or who inspired you to become a statistician?

My colleagues at SAS. Their passion for statistics is infectious.

Did you have a mentor?

Bob Rodriguez, the president of the ASA in 2012. One of Bob’s favorite phrases is “Know your audience.” I use that advice when deciding what features to add to SAS software, when giving professional talks, when writing documentation, and when blogging.

How has the field of statistics changed since you first started your career?

The cheap availability of computational power and memory means that algorithms that were once impractical are now feasible, and nonparametric techniques are now commonplace. Unfortunately, powerful computers also mean that careful thought and analysis are sometimes replaced by brute-force computation, much to the detriment of our profession. Back in 1995, Charlie van Loan at Cornell gave a fabulous expository lecture, titled “If Copernicus Had a Computer,” in which he described basic theoretical advances that might not have happened if computers had been available to one of history’s great scientists. His presentation reminds us that fast computation can be a double-edged sword.

Where do you see the future of statistics going?

Hmmm. Difficult to see. Always in motion is the future.

What advice would you give to young statisticians just beginning their careers?

Never stop learning.

What advice would you give to a student who wants to have the same career path as you?

Get involved in a large-scale, multi-person software project. Find an internship or postdoctoral position that will enable you to say, “I was part of a team that wrote 10,000 lines of code.” To stand out from the crowd, you need more than an R package on your résumé.

In what ways have you benefitted from having an ASA membership?

I became an ASA member in 2003. Being in the ASA helps me keep up with research in computational and graphical statistics.

If you were not a statistician, you would be …?

A math teacher.

What do you enjoy doing in your spare time?

Singing and cooking Italian food—sometimes at the same time!

Name your favorite blogs or books you have read and would recommend to others.

The Theory That Would Not Die by Sharon Bertsch McGrayne is a fascinating account of the history of Bayesian analysis. The Pleasures of Statistics is the autobiography of Frederick Mosteller, a brilliant teacher and researcher and a former ASA president.

What were you doing at age 28?

I was a postdoctoral researcher at The Geometry Center at the University of Minnesota, where I developed several open-source software projects back before open source became cool! I also wrote several interactive web applications during the early days of the Internet.

In 10 years, you hope to be doing …?

The same thing I do today: helping people to analyze and visualize data more efficiently.

Tell us a fun fact about yourself.

I coach high-school wrestling and (of course) keep the team statistics.

Daniela Witten

Education: BS, 2005, math and biology, Stanford; MS, 2006, statistics, Stanford; PhD, 2010, statistics, Stanford
Current position: Associate professor of biostatistics at the University of Washington

What do you do on a daily basis?

There are four major aspects to my job: (1) developing new statistical machine learning methods for the analysis of large-scale data sets coming out of biology and other fields, (2) mentoring PhD students, (3) formal classroom teaching, and (4) collaborating with domain scientists.

There’s no such thing as a typical day! On a given day, I might spend the morning meeting with my PhD students about their statistical methods research projects, the early afternoon working on revisions for a journal submission, and the late afternoon meeting with a genomics researcher about a collaborative project.

Because I mentor a handful of PhD students and have a bunch of ongoing collaborations with other statisticians and with domain scientists, I’m involved in a number of diverse research projects at any given time. This keeps me on my toes and ensures I’m always learning new things. It also guarantees that no two days are alike!

Name a few specific skills you need to do your job.

It goes without saying that, to be a successful statistician, strong technical skills and deep expertise in one’s application area are non-negotiable requirements. But in addition to those skills, I find it is critical to have strong oral and written communication skills, as well as good time management skills.

Oral communication skills are needed to collaborate with statisticians and non-statisticians alike, and to communicate my research findings at seminars, conferences, and other forums.

Written communication skills are key to getting my work published in good journals and lowering the “barrier to entry” for my research papers, so that a larger number of people will read and cite my work. In addition, given the current competitive funding climate, strong written communication skills are critical to craft successful grant applications.

And finally, faculty members have a huge number of draws on their time, such as teaching, mentoring students, collaborating with domain scientists, and more. Good time management skills are critical to being productive in this setting.

What is a skill you would like to learn to be better at your job?

Patience, patience, patience! Getting deeply involved in a new application area takes time. So does the process of getting papers published in statistical journals (for which the entire process from first submission to acceptance can easily take well over a year).

What are the most satisfying and most frustrating parts of your job?

I have been lucky to work with some incredibly talented and hard-working PhD students during my time as faculty at the University of Washington! It’s been both humbling and inspiring to see first-year PhD students develop into mature researchers. And it’s been a lot of fun to get up to speed in new areas and techniques alongside my students.

What or who inspired you to become a statistician?

I figured out pretty early on in college that I wanted to become a scientist, but I had a lot of trouble deciding what area of science to pursue. I didn’t want to be pigeonholed into a particular research area. Instead, I wanted to be able to work on different types of problems at different points in my career (or even at a single point in my career). This line of thinking led me to become a statistician.

Did you have a mentor?

My primary statistical mentor is my PhD advisor, Rob Tibshirani. I learned from Rob to choose research projects that I’m passionate about and to pursue those projects tirelessly.

What advice would you give to a student who wants to have the same career path as you?

I recommend majoring in math in college and becoming deeply immersed in another scientific subject area (e.g., a double major or summer research project). Studying math will ensure that you have the technical skills needed to become a statistician, and exposure to another scientific subject will help you develop the background and communication skills needed to become involved in an application area.

Where do you see the future of statistics going?

Right now, huge amounts of data are being generated across a number of fields, and there is a lot of buzz about Big Data. This is a great opportunity for the statistical community to step in and convince scientists and the broader public that statistics is important and that statisticians (as opposed to, say, computer scientists) are needed to make sense of today’s data.

How has the field of statistics changed since you first started your career?

Statistics has become much more mainstream in the past nine years since I started graduate school! Now, thanks to Nate Silver, the Big Data buzz, the rise of AP Statistics in high schools, and more, people are becoming increasingly aware of the importance of statisticians and statistical methods development in science, industry, and policy.

Within my area of statistical methods research, the types of questions people are answering have changed a lot, too. I work in methods development for high-dimensional data. Five years ago, efforts were centered on estimation in high dimensions. Now the focus has begun to shift toward inference in high dimensions.

In what ways have you benefitted from having an ASA membership?

I joined the ASA early in my graduate school years. I first attended JSM in 2008. The only people there who I knew were the Stanford faculty and graduate students in attendance. I was completely overwhelmed by the huge number of statisticians at the conference. I walked around the Denver Convention Center for hours without seeing a familiar face! Since then, I’ve been at JSM every year but one. Each JSM has been more fun than the last, as I have gotten to know many more people in the field! Now JSM is a great opportunity to catch up with old friends and collaborators and make new ones.

You were on Forbes‘ 30 Under 30 List not once, but three times. Congrats! How does it feel to be recognized like that?

Thanks! It has been fun to be recognized by Forbes three times. It’s really exciting to be a statistician at a time when the scientific community and broader public are becoming increasingly aware of the important role statisticians do and should continue to play in scientific research, public policy, and industry.

What do you enjoy doing in your spare time?

Spending time with my husband and our new baby—and hiking, running, biking, wakeboarding, and paddle boarding in the beautiful Pacific Northwest.

Name your favorite blogs or books you have read and would recommend to others.

A great blog for statisticians to follow is Simply Statistics. Roger Peng, Jeff Leek, and Rafa Irizarry can be trusted to always write timely and entertaining blog posts.

And now for a shameless plug. For anyone (statistician or nonstatistician) looking for a gentle introduction to statistical machine learning, I recommend my recent book with coauthors Gareth James, Rob Tibshirani, and Trevor Hastie: An Introduction to Statistical Learning, published by Springer (and available for free download).

In 10 years, you hope to be doing …?

I’m incredibly fortunate to have a job that I really love! In 10 years, I hope my job will look much as it does now: I will still be working with very smart graduate students and collaborators on exciting and important statistical and scientific problems. But, the scientific problems and solutions I develop to solve them will look quite different from today.

Tell us a fun fact about yourself.

When I started my undergraduate degree at Stanford, I was planning to major in foreign languages! I took my first statistics class at the end of my junior year.