Data Scientist, Owner2018 - PRESENTData Science Consulting LLC
Technologies: Python, Scikit-learn, Keras, Tensorflow, Flask, SQL, Airflow
- 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.).
- Created a detailed plan-of-action that a global IT consulting company presented to multiple front-end and back-end engineers serving as instructions to integrate a new service into their existing platform. Built a substantive prototype to demonstrate functionality to stakeholders in Python. The end-to-end instruction manual included wireframes, data modeling, Monte Carlo simulations, performance testing, and motivation for the project.
- Mapped out a new database system from an existing operational schema for analysts at a leading collections agency to use, which simplified and lead to more robust analyses. (SQL, Airflow).
- Built a flask app for a publicly-traded healthcare company that optimizes efficiency and accuracy when preparing compliance reports. Incorporates human-in-the-loop report initialization, automated querying, task assignment, pdf generation and more. The app is structured to scale with the company at minimal maintenance investment.
- Built a keyword extraction API for the US Government to assist in summarizing and searching a large number of documents using fast variations of many of the popular techniques and case-specific customization, callable with parameters adaptable to users' needs.
- Converted a messy, Excel-based database and reporting process for an investment firm to a scalable, verifiable, and flexible database schema. Created an automated pdf summary report with a range of visualizations visible to key stakeholders.
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.
- 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. The modeling approach performed better on the new dataset.
- Expanded scope of what the 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. The 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. Decreased data entry time and increased accuracy. Displayed results of classification models in an interactive website where users are pointed to areas of low confidence. System started with a small data set, and is built in such a way where models can be retrained from scratch at the click of a button when new data has been validated. (Python, Flask).
- 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 forecasting 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.