Gretchen Riggs

Data Scientist / Machine Learning Practitioner

EXPERIENCE

The Bee Corp, Denver, CO

Data Scientist

Jan 2018 – April 2023

Aided & managed the data science team in the creation of Verifli, a product that grades the health of beehives using infrared hive imagery & machine learning in the AgTech space, going from idea origination to market in less than nine months. Putting into production Queen’s Guard, a product used in evaluating the health of the queen bee, and QGPS, a product used in hive theft prevention. Trained models for Image segmentation. Performed customer discovery analysis, data collection via web scraping & APIs, and created visualizations.

Data Science Consultant - Denver, CO

March 2017 - Jan 2018

Gathered data via web scraping, performed data analysis, made statistical inferences, created visualizations, and interpreted the results in the healthcare, safety, & home improvement industries. ION Geophysical, Houston, TX & Denver, CO

Project Manager/Senior Processing Geophysicist

JULY 2005 - JULY 2016

Performed data analysis, data visualization, anomaly detection, transformation, and signal & image processing on seismic data, managing the project’s status along the way and increasing productivity by 30% through an introduction of more efficient methods. Presented results to clients, communicating in a clear manner the algorithms applied to their data.

EDUCATION

Galvanize Data Science Immersive, Denver, CO

Data Science Fellow

DECEMBER 2016 - MARCH 2017

Fast-paced and hands-on Data Science Immersive Program, grounded in Python.

Missouri University of Science & Technology,

Rolla, MO

B.S. Applied Mathematics

AUGUST 1996 - MAY 1999

CAPSTONE PROJECT

Nature or Not?: Detection of Man-Made Structure from Satellite Imagery — Capstone Project

Training a convolutional neural network to recognize the presence of man-made structures in satellite images from around the globe. Denver, CO

713-***-****

adwuk9@r.postjobfree.com

github.com/gretchenriggs

linkedin.com/in/gretchenriggs

DATA SCIENCE & MACHINE LEARNING

Exploratory Data Analysis, Data Munging,

Web Scraping, Regression, Cross-Validation,

Regularization, Gradient Descent,

Hypothesis Testing, KNN, K-means

Clustering, Decision Trees, Random Forest,

Boosting, XGBoost, SVMs, Neural Networks,

Ensemble Methods, Principal Component

Analysis, AWS, Feature Learning,

Visualization, Anomaly Detection, Image

Processing

PROGRAMMING & ANALYSIS

Python (Pandas, NumPy, SciPy, scikit-learn,

statsmodels, matplotlib, seaborn), Keras,

TensorFlow, SQL/PostgreSQL, Jupyter

Notebook, Git, Github, Unix, C-shell scripting

Galvanize Data Science Curriculum

Statistical Inference, Regression, Supervised

and Unsupervised Learning, Graph Theory,

Recommendation Systems, Data

Visualization, Big Data Processing

Additional Coursework:

Coursera:

Stanford University

Certificate: Machine Learning

PyData:

Web Development for Data Science

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