Scroll To View More
Chris Seal, Machine Learning Developer in Cincinnati, OH, United States
Chris Seal

Machine Learning Developer in Cincinnati, OH, United States

Member since August 13, 2018
Chris is an experienced data scientist with over 4 years' experience working independently and in the government subcontracting space with a leading data analytics firm. His well-rounded university education and work history include a magna cum laude undergraduate degree in physics, a master's in music composition, an advanced degree from Galvanize Data Science Immersive, and a deep learning specialization from
Chris is now available for hire



  • Python 3, 5 years
  • Machine Learning, 4 years
  • Natural Language Processing (NLP), 3 years
Cincinnati, OH, United States



Preferred Environment

Linux, Sublime, Atom, iPython, GitHub

The most amazing...

...result of a project is that I beat ESPN's fantasy football projections and tied Vegas's game winners using raw, unadjusted machine learning


  • Data Scientist, Owner

    2018 - PRESENT
    Data Science Consulting LLC
    • 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.).
    Technologies: Python, Scikit-learn, Keras, Tensorflow, Flask, SQL, Airflow
  • Lead Data Scientist, Owner

    2016 - PRESENT
    Fantasy Outliers
    • 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 (
    • 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.
    Technologies: Python, Flask, R, D3.js, HTML, CSS
  • Senior Data Scientist

    2019 - 2019
    Clarigent Health
    • 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.
    Technologies: Python, SQL, Azure, XGBoost, spaCy, NLP, scikit-learn
  • Data Science Researcher

    2016 - 2018
    Georgia 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.
    • Implemented software that inputs log data and a system definition and outputs an interactive system visualization dynamically changing across time as the user steps through time (mxGraph, Javascript, Python, HTML/CSS). Used to understand complex systems and debug issues within them.
    • 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).
    Technologies: Python, R, Flask, SQL, MongoDB, mxGraph, JavaScript
  • Data Scientist Contractor

    2015 - 2018
    Self-employed (remote)
    • 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.
    Technologies: Python, Flask, HTML, CSS, Machine learning, R, MongoDB, SQL
  • Outbound Business Development + Operations

    2014 - 2015
    Connect First
    • Created foundational methodologies for a new lead generation department, which led to better sales and more internal funding for our department.
    Technologies: Excel, Phone
  • Composer, Founder

    2010 - 2015
    • Developed project management and relationship building skills with clients, maintaining profitable, repeat-customer business, and 5-star rating.
    Technologies: Music composition
  • Business Development and Music Production

    2012 - 2014
    • Grew list from ~100 to 900+ organically developed, active contacts in 12 months through introductory meeting generation with top-tier advertising agencies.
    Technologies: Music composition, Sales
  • Senior Diagnostic Consultant / Database Analyst

    2005 - 2008
    The Nielsen Company
    • 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.
    Technologies: Excel, SPSS


  • Fantasy Football Predictive Models Beat ESPN, Tied Vegas (Development)

    Last year, Fantasy Outliers’ predictive models helped a disproportionate number of users win their leagues, spotted Free Agent pickups a week or two before others started talking about them, gave good start/sit direction. When compared to ESPN's projections, yearly overall rankings were more accurate than ESPN’s 72% of the time and were directionally accurate 84% of the time for quarterbacks. Weekly projections were more accurate than ESPN's 57% of the time and directionally accurate 64% of the time for quarterbacks who were likely starters. Other positions were less accurate, but still better than ESPN often.

    In 2018, we implemented a game winner prediction model that predicted NFL game winners with information available Tuesday morning that ended up tying Vegas's predictions that used information available up until kickoff.

    Full write-ups include, How Artificial Intelligence (AI) beat ESPN in Fantasy Football ( and Can machine learning help improve your fantasy football draft? (

  • Attributing Flowrate Signal to Devices Using Data Sensors (Development)

    For a capstone project at Galvanize, built a system that uses data from sensors to analyze energy efficiency. The system can determine what devices or appliances are currently turned on and the resource demands attributed to each device, allowing for further usage optimization downstream.


  • Languages

    Python 2, Python 3, SQL, JavaScript, HTML, R, CSS5
  • Libraries/APIs

    Scikit-learn, XGBoost, Matplotlib, TensorFlow, Keras, D3.js, jQuery
  • Tools

    NLPP, MxGraph
  • Paradigms

    Data Science, Object-oriented Programming (OOP), Agile, Anomaly Detection
  • Other

    Data Visualization, Machine Learning, Natural Language Processing (NLP), Speech Analytics, Algorithms, Data Mining, Data Analytics, Software Development, Deep Learning, Agile Data Science, Convolutional Neural Networks, Time Series Analysis, Sentiment Analysis
  • Frameworks

  • Platforms

    Linux, Windows
  • Storage

    MongoDB, NoSQL, PostgreSQL, AWS S3


  • Master's degree in Music Composition
    2005 - 2007
    University of Louisville - Louisville, KY
  • Bachelor's degree in Physics, Music, Psychology (minor)
    2000 - 2004
    Wake Forest University - Winston-Salem, NC
  • Deep Learning Specialization
  • Data Analyst
    APRIL 2016 - PRESENT
  • Data Science Immersive Bootcamp
I really like this profile
Share it with others