James Woodruff

Data Scientist GitHub, Linkedin

203-***-**** adjo1j@r.postjobfree.com

Skills

Skills: Machine Learning/Deep Learning, Analysis/Data Mining, and Data Visualization with Python, R, SQL, Spark, Flask, and Microsoft Office

Experience using Azure, AWS, SNL, Factset, Capital IQ, Salesforce. Work Experience

Data Scientist Health Catalyst September’18-Present

• Lead data science on Touchstone product line

• Created 30 and counting machine learning models for risk adjustment and benchmarking

• Conceptualized and implemented production model deployment pipeline

• Created and automated final data validation process

• See Below

Associate Data Scientist January-August 2018

• Hospital and Health Care data exploration and analysis

• Iterated and improved previous models through feature engineering and parameter tuning

• Researched competitors’ methodologies and implemented in order to assess Health Catalyst’s market position

• Researched and implemented Deep Learning algorithms with TensorFlow/Keras Data Science Intern June – December 2017

• Worked under Touchstone’s lead data scientist

Express Implementation Consultant Xactly Corporation December – November, 2015-2016 Xactly Corporation is Software as a Service company focused on incentive compensation.

• Analyze client’s incentive compensation plans to reduce unnecessary loss

• Troubleshoot configuration errors and identified bugs

• Product testing of new releases

Summer Analyst Stone Point Capital May - August, 2014 Stone Point is a private equity firm that has raised six funds with aggregate committed capital of $13 billion with additional equity co-investments of approximately $6 billion.

• Presentations for potential REIT investments

• Set up comprehensive research reports, targeting acquisition history

• Sent out a weekly firm-wide capital markets analysis and updates

• Compiled statistical information and key performance indicators (KPI’s) for firms within Stone Point’s portfolio

Education

University of Utah Salt Lake City, UT

M.S. Computer Science 2019-present

Relevant Coursework: Machine Learning, Data Visualizations, Data Mining, Probability Vanderbilt University Nashville, TN

B.S. Mathematics 2011-2015

Relevant Coursework: Linear Algebra, Differential Equations, Number Theory, Advanced Problem Solving, Statistics, Multivariable Calculus and Optimization B.A. Economics

Relevant Coursework: Public Finance, Money and Banking, Economic Statistics Projects (also found on GitHub)

Old Bailys (Kaggle)

Used gradient boosted trees to classify the verdict of trials from the Old Bailys court, using transformed representations of the court dialogue and demographic features of the victims/perpetrator.

Crime Resolution Predictor

Used a Random Forest Classifier to predict whether an unresolved crime will get resolved at a later point, through the use of natural language processing, dimensionality reduction, and feature engineering.

Fraud Detection

Created a fraud detection application hosted online to predict probability of fraud for incoming music events. Eventually decided on using a gradient boosted classifier over a support vector Price Modeling Case Study

Used Logistic Regression and feature engineering to predict the price of different tractors. Recommendation Systems

Created recommendation systems, eventually deciding on factorization to recommend jokes based natural language processing and user joke ratings ranging from -10 to 10.

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