Emily K. Sanders | LinkedIn
Dear Recruiters,
My name is Emily K. Sanders, and I am currently looking for work in data science or a related field. This repository contains my current resume and CV, as well as any other documents that it makes sense to publish in a public space. I am happy to provide additional documentation, including transcripts from undergraduate and graduate school, in application packages.
Please read on for a further discussion of my work and interests, or feel free to contact me via GitHub or LinkedIn to schedule a one-on-one conversation.
Thank you,
Emily K. Sanders
Sanders, E. K., & Chalk, H. M. (2016). Predictors of psychological outcomes in nonheterosexual individuals. Psi Chi Journal of Psychological Research, 21(2), 100-110. https://doi.org/10.24839/2164-8204.jn21.2.100 | Link, PDF
Dai, W., Yang, T., White, B. X., Palmer, R., Sanders, E. K., McDonald, J. A., Leung, M., & Albarracín, D. (2023). Priming behavior: A meta-analysis of the effects of behavioral and nonbehavioral primes on overt behavioral outcomes. Psychological Bulletin, 149(1-2), 67-98. https://doi.org/10.1037/bul0000374 | Link
Dear Recruiters (again!),
Thanks for reading on! Here's some more about me.
I am drawn to roles that offer an opportunity to use my statistical and research skills to address real-world issues impacting regular people and communities. Whether I was conducting research on the harmful effects of anti-queer bias, organizing labor unions, or making coffee for my neighbors at the local "everything store," all of the best parts of my previous jobs have been the moments of connecting with other people. I am passionate about work that has a genuine societal impact for good, and eager to leverage my Masters of Science in social psychology and extensive training in data science towards this goal.
As a researcher, I designed and implemented studies using various methodologies, all involving gathering and analyzing data, applying appropriate statistical analyses, interpreting the results, and communicating my findings to others. I presented my methods and findings at professional society meetings, in lab and department reports, and in two journal articles. I was first author on one of the journal articles and all of my posters, and gained recognition as an exceptional technical writer who could present complex ideas in an understandable way. My 2016 paper, Predictors of psychological outcomes in nonheterosexual individuals (Sanders & Chalk), is a good example of this work.
During this same time, as a graduate teaching assistant for 3 undergraduate classes, I took the initiative to instruct my students on statistical and research methods beyond the base requirements of the class, including data analysis and statistical software. Although these lessons were dense and at times daunting for students with no technical background, I felt strongly that understanding how scientists draw inferences from data would allow my students to better contextualize all other concepts in the course. This depth proved useful to my students as I advised them through the process of developing, conducting, interpreting, and reporting their own research projects.
After graduate school, I worked several rewarding jobs, but found that I still longed for research and the production of knowledge. Therefore, I enrolled in the data science immersive program at General Assembly, where I expanded and refined my skills, and learned how to apply them in non-academic settings. I completed multiple open-ended projects using data from multiple sources, and applied analytical skills ranging from basic descriptive statistics to multivariate modeling. One example of this work is my It Takes a Village project, accessible on GitHub as CA_dropout. The objective of this project was to build a model of high school dropout rates in California from factors like poverty, health care access, prevalence of smoking, and more. I worked with a team to clean, manipulate, and evaluate the data, and to identify potential problem areas in our model. I took the lead on producing our technical report and presentation, in which we suggested that fitting the model on more granular data could resolve most of those problem areas. Post-graduation, a teammate and I am revising the model to use data from individual school districts rather than entire counties.
Thank you for taking the time to review my materials. I am sure I could be a valuable addition to your team, and look forward to further discussing my background and qualifications soon. I can be reached on GitHub or LinkedIn at your convenience.
Sincerely,
Emily K. Sanders