Lauren D

Salt Lake City, Utah United States

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Summary

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Rockstar

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Top School

Lauren D is a data science storyteller who blends a MS in Geophysics with content marketing experience to turn complex data into clear, actionable narratives. With a decade of experience spanning geophysics, science journalism, and marketing analytics, she is fluent in Python, R, Matlab, Pandas, Scikit-Learn and H2O.ai for building and validating models. She has contributed to the widely used H2O-3 open-source project, adding robust tests and documentation to improve handling of unseen categorical levels. Based in Salt Lake City, she pairs technical rigor with marketing tools like Google Analytics and Salesforce to bridge insights and go-to-market impact. Lauren’s background in field science and outdoor education gives her a practical, curious approach to problem-solving and a knack for making technical results relatable to non-technical stakeholders.

10 years of coding experience

Bachelor of Arts (B.A.), Geophysics, Bachelor of Arts (B.A.), Geophysics at University of California, Berkeley

Masters of Science, Geophysics, Masters of Science, Geophysics at Scripps Institution of Oceanography

Outdoor Education, Outdoor Education at NOLS

German, Spanish, French

Github Skills (9)

machine-learning10

h2o10

python10

data-science10

unit-testing9

pandas8

numpy8

automated-machine-learning7

automl7

Programming languages (3)

ScalaJupyter NotebookPython

Github contributions (5)

h2oai/h2o-3

May 2016 - Apr 2019

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Role in this project:

Data Scientist

Contributions:1 review, 109 commits, 47 PRs in 3 years

Contributions summary:Lauren contributed to the H2O-3 repository by adding a Python unit test for the H2OFrame class, focusing on testing the functionality of the H2OFrame. The code changes involve importing necessary libraries like h2o, numpy, and pandas, and testing aspects such as the frame's dimensions with various input data structures. The user also updated the documentation, specifically for the example code. Further, the user added examples to show prediction with unseen categorical levels.

xgboostgampythonk-meansautoencoders

laurendiperna/h2o-3

Feb 2016 - Apr 2016

Contributions:7 pushes, 1 branch in 1 month

apiscalablepythonsmarterdeep-learning

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