Data Scientist, Owner2018 - PRESENTData Science Consulting LLC
Technologies: Python, Scikit-learn, Keras, Tensorflow, Flask, SQL, Airflow
- Created a data as a service solution for small and medium-sized businesses.
- Provided end-to-end automated solutions involving data acquisition, database setup+maintenance, exploratory analysis, dashboards/data visualizations, machine learning for predictive and unsupervised modeling, and web apps for any type of data (text, time series, tabular, etc.).
Lead Data Scientist, Owner2016 - PRESENTFantasy Outliers
Technologies: Python, Flask, R, D3.js, HTML, CSS
- Provided historical and predictive analysis for fantasy football.
- Beat ESPN's weekly projections in Weeks 6-16 of 2017.
- Predicted several key underrated players in 2017 (Russell Wilson, Zach Ertz, Mark Ingram) and quarterback projections beat expert consensus rankings.
- Explored what actually happened in competitive leagues with interactive visualizations (fantasyoutliers.com).
- Tied Vegas's up-to-kickoff game winner projections using automated predictions based on data available Tuesday morning the week prior with no manual adjustments for injury, etc.
Senior Data Scientist2019 - 2019Clarigent Health
Technologies: Python, SQL, Azure, XGBoost, spaCy, NLP, scikit-learn
- Improved status quo of published, patented suicide ideation classification model by ~10-15%, based on leave-one-out validation. Modeling approach performed better on new dataset.
- Expanded scope of what company previously thought was possible to predict. Built successful models in areas they hadn't previously thought possible.
- Built pipeline from scratch that includes version-controlled, advanced NLP feature engineering, dynamic/"smart" pre-processing with dimensionality reduction, concurrent hyperparameter search and feature selection for both regressors and classifiers, model explainability, and insights across multiple models. Approach is dynamic, allowing users to align modeling approach with the dataset and project constraints.
Data Science Researcher2016 - 2018Georgia Tech Research Institute
- Analyzed team cohesion in League of Legends Matches. Implemented automated data-collection pipeline in MongoDB with >3TB of data of League of Legends match data. Used PCA, K-Means clustering, network density, and others to develop non-skill-based features from a psychological perspective that discriminated between wins and losses. Trained Gradient Boosting Classifier to predict the game winner based on historical psychological dimensions across the team (non-skill-based) with some success (AUC 0.58-0.68).
- Automated data acquisition, cleaning, merging, and visualizing various publicly available data breach sources, creating a more reliable and complete data source. Created an automated engine using web scraping and NLP to gather and search SEC filings for language containing a high probability of data breach cost disclosures.
- Built compliance risk metric for government facilities using multiple, auto-trained and aggregated XGBoost models to help prioritize government resources (NLP, NNMF). Built automated, cross-document named entity analysis pipeline, using spacy and Python, for count-based association analysis.
- Built software, inspired by Continuous Integration platforms, that builds, runs, and assesses granularized performance of a script across all function calls (Python). Links to git repository and runs with every commit, comparing performance to previous commit, and raises alerts if performance dips below user-defined thresholds. Visualizes performance history in a dashboard (Flask, SQLAlchemy).
Data Scientist Contractor2015 - 2018Self-employed (remote)
Technologies: Python, Flask, HTML, CSS, Machine learning, R, MongoDB, SQL
- Built automated information extraction engine for unstructured financial statements using a unique pipeline of tree-based ensemble classifiers. Enabled company to engage in more complex historical analyses.
- Created a Monte-Carlo-based pricing simulator that provides insight into both portfolio-wide and individual client pricing strategies with very little information about the customer. Expected profit simulated distributions combined with visualizations helped pricing team understand probabilistic expectations for a given customer, which lead to better client relationships. Built an automated system that forecasted eligible assets, which led to higher profits.
- Implemented first-of-kind program that analyzed signal rate data using a sequence of Random Forest Classifiers and logic to attribute signal load to individual devices and analyze results. Continued work on capstone project through prototype completion.
Outbound Business Development + Operations2014 - 2015Connect First
Technologies: Excel, Phone
- Created foundational methodologies for a new lead generation department, which led to better sales and more internal funding for our department.
Composer, Founder2010 - 2015Tuneplant
Technologies: Music composition
- Developed project management and relationship building skills with clients, maintaining profitable, repeat-customer business, and 5-star rating.
Business Development and Music Production2012 - 2014alcheh&hunt
Technologies: Music composition, Sales
- Grew list from ~100 to 900+ organically developed, active contacts in 12 months through introductory meeting generation with top-tier advertising agencies.
Senior Diagnostic Consultant / Database Analyst2005 - 2008The Nielsen Company
Technologies: Excel, SPSS
- Worked with VP’s and C-Level executives to create and implement a comprehensive quantitative and qualitative framework describing the consumer adoption process.
- Used Excel and SPSS to craft data-driven responses to inquiries regarding historical database and to conduct research, which resulted in internal recognition of achievement award.