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Interview: Early Career Quantitative Scientists

We reached out to some colleagues who are early career scientists in quantitative fields and asked them to share their experiences.

 

Briefly, who are you and what do you do/study? How long have you been working in your current position?

 

 

 

 

I am Juniper (they/them) Simonis. I am a data analyst in the Weecology Lab at University of Florida and a freelance scientist with my own company DAPPER Stats. I started DAPPER in Spring 2015 and began working at UF in late Fall 2017. My work is at the interface of math and biology, and I am particularly focusing on using contemporary statistical and modeling methods to discern the causes of population dynamics (usually in species of conservation concern). Twitter @DapperStats

 

 

 

 

 

I am Christa Brelsford, the Liane B. Russell Fellow at Oak Ridge National Laboratory in the Geographic Information Science and Technology group. My core research goal is to develop empirical methods to understand interactions between human and physical systems, especially in an urban context.  I use empirical methods like spatial analysis, network analysis, and remote sensing to explore the shape and topology of cities and neighborhoods. My research has been applied to problems of water demand, water institutions, and informal settlement upgrading.

 

 

 

 

 

I'm Amy Willis, an Assistant Professor in the Department of Biostatistics at the University of Washington. I develop statistical tools to help scientists analyze and visualize their data. I have a particular interest in data from microbial ecology because microscopic organisms that control our health and environment are amazing! I have been doing this kind of work for about 6 years and arrived at the University of Washington (UW) in July 2017. Twitter @AmyDWillis

 

 

 

 

 

 

 

 

What most excites you about the work you do?

 

JS: Getting to use nerdy math to help endangered species and helping young scientists understand the role of math in conservation. We often think about math as this abstract, perhaps not-directly-useful skillset ("when will I ever need to know how to integrate something??"), but it is crucial in managing natural resources and conserving imperiled species.

 

CB: I care about people.  I am proud to be an American citizen, and I can’t think of a better way to spend my time than trying to understand, prevent, and mitigate the biggest risk our country faces: climate change. I grew up in rural Alaska, not far from the places that are now producing America's first climate refugees, as climate change-driven coastal erosion literally wipes their villages off the map. I spent a decade living in the desert Southwest, where allocation institutions that were appropriate for last centuries' problems are creating a situation where one more drought will cause major economic upheaval. These are complicated hydrological and environmental systems, but the problems we face because of changes in these physical systems will only be solved through intervention in human social structures: our laws, policies, and behaviors. We cannot understand the complex and interdependent world we live in without developing models that acknowledge the role of human social structures, choices, and behaviors.

 

I think that the National Labs have a responsibility to our country to do the best science we can. I hope that I can be a leading part of the team of people doing the social science research necessary to help our society figure out how to create the smooth and sustainable institutional transitions that are necessary to allow the global climate to remain as stable as it can. Additionally, I want to facilitate transitions towards resilient and adaptable social systems as we adjust to higher variability in a broad range of environmental, ecological, and hydrological conditions.

 

AW: I work with some amazing scientists and incredible technologies. I'm grateful to have skills that I can use to build analysis tools that can help researchers with their data. A lot of the time I collaborate with friends, which is always riotously fun. In addition, as a statistician, I'm often analyzing data for collaborators, and I get to see amazing signals in the data before anyone else. Statisticians can connect scientists with scientific truths, and I love facilitating and enabling that connection.

 

Is your current job the one you imagined having? If yes, how did your training/education prepare you? If no, has your training/education prepared you in ways that have surprised you? Maybe a bit of both?

 

JS: Not exactly. When I started out in grad school, I thought I would go down the academic track and eventually be a tenure track professor. However, by the end of grad school, I was disillusioned with academia and wanted to see what other avenues existed for being a professional scientist. I had a postdoc at a zoo and worked for a consulting company for a bit before striking it out on my own, which I basically had zero training directly in. I leveraged a handful of non-scientist friends and family members who set up their own businesses to get insight into freelancing and being an entrepreneur, but have had to learn a lot of it as I go.

 

CB: I literally wrote the job description for my current fellowship, so I think that the work I get to do right now is pretty great. I spent a substantial part of graduate school in at the Los Alamos National Lab, which gave me a good perspective on the differences between the Lab system and the academic system, and that was very helpful for me in making an informed decision about the pros and cons of the different settings. Right now, I am still closely tied to academia and publishing papers at a rate that’s comparable to what I would be doing in a university, so I (theoretically) could transition back to academia. My research right now is directly related to my graduate work, both from a methods perspective and an application perspective.

 

AW: When I was 14, I decided that I wanted my life work to be creating new knowledge (my poor parents -- I was a handful!). I asked around and found out that people who did this were called academics, and I decided I wanted to be one. I also found out that many of the best researchers within the U.S., and so I decided that someday I wanted to go to graduate school in the U.S. I was 14. In hindsight, it sounds so silly -- there are many different places where people create new knowledge other than academia, and there was no reason why I had to study in the U.S. Alas, I get pretty fixed on goals, and it miraculously turned out that the plan that I created way back was the one that I took.

 

I originally wanted to be a geologist, then a physicist, and then a mathematician. I got a scholarship to read mathematics in Canberra, Australia, but discovered quickly that most mathematicians aren't solving problems that are strongly connected to other scientific fields. I switched majors to statistics, which was the best decision I could have made. I love being a statistician, I love working with data, I love creating tools, and I love collaborating with scientists.

 

I was woefully unprepared for graduate school in the U.S., and I really struggled when I first arrived. Most of my classmates had strong theoretical math training, and funnily enough, a specialist degree in statistics barely prepares you for a PhD in statistics. I was so frustrated with this that I got my teeth into as many collaborative projects as I could to keep myself motivated. At that time, I was really fortunate to meet John Bunge, who would be my PhD advisor. John taught me to see the value in theory and the importance of a solid theoretical foundation for applied statistical work. One of my mentors in Australia told me to choose a thesis advisor that I liked, rather than a thesis advisor whose research I wanted to do -- that was fantastic advice. John and I have incredibly different research passions and incredibly different research styles, but he taught me a great deal about challenging myself and rethinking previous biases.

 

Overall, I learned a lot of different skills over different times, and I'm drawing on them all in my current job. My applied training as an undergraduate helps me just as much as the theoretical training that I received in graduate school. I think the fact that I really struggled at the start of graduate school, and that I really struggled to get my first few papers published, means I'm a lot more resilient than my peers for whom this came easy.

 

There is a lot of pressure in science to pretend that hard things are easy, which drives a lot of people (especially women) out of science. I'm actually really proud that I struggled through graduate school, and I want to use my experience to convince young scientists that the classical ideal (that great scientists must show early promise in order to be successful later) is complete rubbish. Most of the scientists that I respect weren't stars in undergraduate or graduate school -- resilience and hard work are more valuable skills than technical ones.

 

As a gender minority in a STEM field do you feel supported by colleagues, advisors, and/or collaborators? If you've had varied experiences, what contributed to more positive or negative relationships?

 

JS: In general, I tend to carve out spaces for myself where I can feel safe and supported, so I tend to cut folks out of my life professionally and personally if they don't treat me appropriately. I just don't have the capacity to engage with folks who see me as “other” before they see me as a scientist. Like there are people who have known me for over a decade who use my deadname and mispronoun me intentionally, and I just don't have time for that. At the same time, I have been blessed with the presence of great and supportive folks in my work life…but I have had to work extra hard to find them.

 

CB: In the position I’m in now at ORNL, my mentors, managers, and advisors are all helpful and accessible and have been very pro-active in acknowledging and helping me strategize for how to problem solve whichever problems I’m concerned about. This has included both very typical technical concerns (where should I submit this paper, do you know of anyone here who has expertise in X), managing the transition to the lab system, and more personal and gender-based issues that are relevant to my life stage. The current biggest source of work/life balance stress is what to do when illness and bad weather disrupt the usual childcare routines. My management has been very supportive of the flexibility I’ve needed to manage this. In the wake of the #MeToo movement, my management explicitly asked me, in an appropriate and private setting, if I have any concerns about people here, and I feel confident that if I did have any concerns (I don’t) they would be taken seriously. This group is also by far the most racially diverse group I’ve ever worked in in academia, and my guess is that the unique issues that people of color in the group face are handled with similar care and skill. As an example: a group leader showed a 20 minute video addressing diversity and implicit bias, which was followed up with a discussion. This took a significant amount of staff time, and I really appreciated the serious commitment that it implied towards treating every person fairly, while acknowledging the many intersecting forms of identity we all have.

 

​​In a previous setting, the environment was very different. The institution failed to acknowledge the differences in how woman navigate the professional world, leading to a situation in which people there at all levels were unable or unwilling to acknowledge the implicit biases that we all have. This lead to a situation where fewer women wanted to work in that environment, women are evaluated more harshly there, and the women who are there are less able to speak up about the problems, because we are (legitimately) concerned that this will be taken as further confirmation of our lack of academic ability there. (For anyone reading my CV who has guessed which institution I’m talking about, I have expressed my concerns to the faculty who seem potentially receptive, and some are making a substantial effort to make the institution more friendly to new parents.)

 

I’d been told to clear out my desk when I took time off for childbirth “in case I didn’t come back.” There was no direct acknowledgement of the unique issues I faced as (at the time) the only female postdoc, especially in the context of my recently having given birth. To my knowledge, I was the first person to give birth while a scientist there, and my pregnancy and childbirth were regularly the subject of comments, that although they were well meaning, were ultimately hurtful.  (“You look so uncomfortable, you need a cart to hold up that belly.”) As other female postdocs were hired (for a brief time they were up to six, although the numbers there are now back down to one), myself and others were mistaken for undergraduates, visiting girlfriends, and other non-scientist roles. I experienced several cases where women had given talks, and at the end of the talk, some famous male scientist would make a comment along the lines of “Well, this is very nice, but you are asking the wrong question. You really should be studying….  (something completely different).”  I was in a talk where a well-respected visiting scientist said something along the lines of, "well, I’m not really sure we want 50% representation of women in (this field). There are a lot of reasons why women might not want to or be capable of entering the field” in response to a paper estimating the effect that gender has on academic hiring practices. It took far too long for the faculty members who were also listening to the talk to acknowledge the problems inherent in that comment.  These are some of the many examples of gender based bias I observed there, and my guess, given the paucity of people of color at the institution, is that issues of racial bias would also be a significant problem in the institutional culture. 

 

The difference in culture between the two institutions made a very significant difference to my overall happiness at work, my ability to participate in the broader intellectual environment, and ultimately my research productivity.  Proactive management and asking questions on a regular basis, rather than expecting me to bring up my own concerns and have to justify that they’re something that should be addressed makes a huge difference, as has a culture of respect and humility.

 

AW: I really do feel supported by my colleagues and collaborators at UW. I've worked in settings before where I felt the opposite, so I really appreciate it now. I went to an all-girls high school, and all of my math and science teachers were women, so I didn't realize that women were negatively stereotyped in math until my first day as an undergraduate, when the undergraduate math advisor told me that 1st year women typically don't do well in 2nd year calculus, and that he didn't think I should enroll alongside the 1st year men who had the same high school grades and scholarship I did. That was pretty jarring, and I saw much worse in the following years. I've worked in some hostile environments, but throughout I have always been lucky to find mentors who supported me and who coached me through negativity. That's what got me through.

 

Anything else you'd like to share about being an early career scientist, or more broadly?

 

JS: I think it's really important to not limit the options you see for yourself. There are myriad ways to apply the skills (tangible and intangible) we learn in science and academia to the broader world, including to different career fields.

 

CB: I had two babies in the early part of my academic career: one 4 months before my PhD defense and one at the tail end of a postdoc. Having little babies early in my academic career was really hard, but they’re getting bigger and more independent now, and I really can’t imagine my life without them. They’re kind, gentle, wonderful children and to a certain extent, I feel like I’m doing the science that I do in the hope that they, and any kids they might chose to have, can live in a safer, more stable, more just world.  

 

AW: When I was a graduate student, I used to push back a lot. My advisor and I used to argue a lot about the best way to approach a research problem, or the best explanation for strange results. In hindsight I wish I was a bit more professional, but John really encouraged the pushback, and it means that now I can really separate my research ideas from his. There is so much pressure for graduate students to please their advisers, but I really want junior scientists to learn to push back and develop their own opinions and approaches. So, if you're a junior scientist, and tend to defer to your mentors, think about practising pushing back when you disagree or have a different explanation. There is rarely only one way to do something -- try yours!

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