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<p>I’m a data scientist with an MS in statistics and a commitment for ensuring model appropriateness, validity and performance. In my view, this starts with understanding the question of interest and the data being used to answer it. </p>
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<p>In my 5 years working in scientific research, I have honed the ability to holistically understand a specific question within a complex context, write and speak clearly, and use computational tools (Python, R, Docker, Linux and AWS/HPCs) effectively. I bring both a big picture perspective and an eye for the details. </p>
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<p>Outside of work, I enjoy cooking fresh veggies, walking and reading.</p>
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<p>I'm currently seeking opportunities for my next full time role. </p>
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<p>I am a data scientist with hybrid interests in methods for large numeric data, engineering machine learning solutions for these data, and collaborating in diverse teams. My interests in numeric data originated in bioinformatic data while studying statistics at Brown University, and I then followed this path to a Harvard affiliated research institute working with several types of ‘omics data, motivating me to attend graduate school in statistics at Oregon State University. </p>
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<p>With an MS in statistics, I continued to Pacific Northwest National Lab in Seattle, WA, where I worked in both Data Science and Software Engineering teams, implementing containerized and deployed workflows using high dimensional numeric data, including a dashboard with on-demand model retraining for a dozen distinct prediction methods, and a data engineering project standardizing multiple disjoint sets of data for deep learning predictions on the combined data.</p>
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<p>As a collaborator, I am skilled at understanding and interpreting leadership goals and translating them into a feasible technical solution accomplishable in a timely manner. I prioritize mentoring, sharing knowledge and asking questions. </p>
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<p>My primary professional goal is to engineer reliable, precise, well-designed machine learning solutions as a trusted team member in contexts with high-volume streams of numeric data. </p>
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