Eleanor Thomas

Eleanor Thomas

Senior Data Analyst @ Doximity

About Eleanor Thomas

Eleanor Thomas is a Senior Data Analyst with extensive experience in data analytics and education. She has worked at various organizations, including Doximity and Springboard, and holds multiple nanodegrees from Udacity in data science-related fields.

Current Role at Doximity

Eleanor Thomas serves as a Senior Data Analyst at Doximity, a position she has held since 2019. Working remotely, she focuses on data analysis to support healthcare professionals. Her role involves leveraging data to drive insights and improve decision-making processes within the organization.

Previous Experience in Data Science Education

Eleanor worked at Thinkful as a Data Science & Data Analytics Educator from 2019 to 2021. In this remote role, she taught students the fundamentals of data science and analytics, helping them develop the skills necessary for careers in the field. Her experience in education complements her analytical expertise.

Background in Financial Markets

Eleanor has a background in financial markets, having worked as a Trader (Data Analytics) at IMC Financial Markets & Asset Management from 2012 to 2015 in Chicago, IL. She later served as an Options Quantitative Researcher at Old Mission Capital LLC from 2016 to 2019, where she applied data analytics to optimize trading strategies.

Educational Qualifications

Eleanor holds a Bachelor of Science (B.S.) in Mathematics and Philosophy from Harvey Mudd College, which she completed from 2008 to 2012. She has also pursued several Nanodegrees from Udacity, including Deep Reinforcement Learning, Natural Language Processing, and Data Scientist, enhancing her skills in various areas of data science.

Mentorship and Curriculum Development

In addition to her role at Doximity, Eleanor has been a Data Science & Data Analytics Mentor at Springboard since 2019. She contributes to the design and updates of the curriculum for online Data Science & Data Analytics Career Track bootcamps, focusing on best practices in automation and testing within data projects.

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