Hi! I'm an individual who's passionate about the field of data science and everything numbers related. I'm currently a data associate at Pearl Meyer and am also pursuing a Master's Degree in Data Science with a focus on Data Engineering at Northwestern University. As part of my undergraduate studies I graduated with bachelor's degrees in Econometrics and Quantitative Economics and Urban Studies and Planning. My academic career and studies focused on quantitative analysis, cost/benefit analysis of public policy and business decisions, and drawing relevant conclusions from data analysis and visualization. This, in combination with my time spent working as a data analyst and research data assistant, has fostered a passion for data analysis and market research. My future career goals include continuing to work as a data analyst and eventually become a data scientist to better fully develop and apply my quantitative analysis skills, data visualization skills, and technical programming skills.Currently my position as a data associate in the compensation consulting industry involves utilizing various technologies and data sources to collect and clean relevant data as it pertains to the client's project. I then work on synthesizing meaningful conclusions from these datasets and create various forms of data visualizations to help present the information in a concise and easy to follow manner for our clients. All of this while using effective time management and communication skills with project team members to balance shifting deadlines, priorities and project deliverables.My time as a research data assistant I helped a faculty researcher in creating and compiling course-related material pertaining to evaluating true causal effects of policy or business decisions on markets. This included creating an interactive and online HTML RStudio "bookdown" for students of the class to use in order follow along with the several research and data analysis techniques used in economics research in order to get causal effect, such difference-in-difference tests (A/B testing), robust linear regression analysis, and synthetic control analysis. This process also included cleaning and publishing large data sets and RStudio code in a visually appealing manner.I look forward to getting in touch with you!
