Christopher Bukowski, Data Scientist and Developer in Denver, CO, United States
Christopher Bukowski

Data Scientist and Developer in Denver, CO, United States

Member since January 27, 2020
Chris is a data scientist with a decade of experience split between academic and professional settings. He specializes in creating predictive models to solve unique and interesting problems. He uses his knowledge and expertise to provide data driven guidance that enables businesses to grow and advance. Freelancing allows him to expand his knowledge and work on challenging and unique problems in varied domains.
Christopher is now available for hire

Portfolio

Experience

Location

Denver, CO, United States

Availability

Part-time

Preferred Environment

Python, R

The most amazing...

...model I've created accurately forecasted the price of electricity up to a week in advance and produced annual returns of over 200%.

Employment

  • Senior Data Scientist

    2012 - 2019
    MWD Trading, LLC
    • Created models to predict price movements in highly liquid commodities, futures, and power markets, which consistently produced returns in excess of 75% annually.
    • Implemented and maintained all internal reporting for business and trade development, including deploying and managing an internal website which provided on-demand reporting using the R Shiny platform.
    • Managed financial risks associated with commodities and futures positions held by the firm across all markets and exchanges.
    • Developed programs and processes to reduce human evaluation of data and quantify subjective analysis.
    • Worked closely with management to provide analysis and data-driven recommendations for business growth and development.
    Technologies: Microsoft Excel, Scala, Java, Python, R
  • Teaching Fellow

    2010 - 2012
    Colorado School of Mines
    • Developed curriculum for and implemented graduate-level statistical computing course taught to incoming graduate students.
    • Constructed and taught undergraduate statistics courses focusing on theory and application of traditional statistical techniques and the technologies used to implement them.
    • Worked in a team environment to define teaching objectives for undergraduate statistics courses.
    Technologies: LaTeX, Linux, SAS, Minitab, MATLAB, R

Experience

  • High Frequency Trading Strategy (Development)

    This was a strategy that could be used to market make highly liquid commodities and futures markets. It focused on market microstructure to inform the model and make forecasts. I was in charge of building and testing the model that was used to predict price movements over the next second. The general framework for this strategy was used to create models for over 20 different markets. In its more than six years in production, it consistently produced annual returns greater than 75%.

  • Power Market Price Prediction (Development)

    The goal of this project was to accurately predict day ahead and real time prices in various power markets. Prices were predicted one day to one week into the future. It employed machine learning techniques taking into account the temporal nature of power data. Data was aggregated from various sources (ISOs, weather data, ICE market data) to create the final model. The project led to a pricing model that accurately forecasted market prices, producing annual returns of around 200%.

  • Power Load Curve Classification (Development)

    The project involved creating a script to automatically classify load forecasts for various power markets. Load forecasting is an integral park of any price model in power markets, and as such, correctly classifying the type of curve is crucial. Accurately classifying the shape of the curve was done to remove any human subjectivity and aid in automating manual processes.

  • Reporting Web Page (Development)

    This was a web page that provided on-demand reporting about all current and past trading strategies. I was in charge of building and maintaining this site. The site was built using the R-Shiny platform. It provided users a quick look at any trading strategy over the previous six months. Through a series of widgets and dashboards, it allowed users to quickly monitor business and trade performance and make data driven decisions going forward.

Skills

  • Languages

    R, Python 3, SQL, Python, SAS, Java, Scala
  • Tools

    R Studio, Microsoft Excel, MATLAB, LaTeX
  • Paradigms

    Data Science
  • Other

    Data Visualization, Data Analytics, Statistics, Commodity Trading & Risk Management (CTRM), Machine Learning, Financial Data Analytics, Minitab
  • Frameworks

    RStudio Shiny
  • Platforms

    Jupyter Notebook, Linux
  • Industry Expertise

    Project Management

Education

  • Master's degree in Applied Mathematics and Statistics
    2010 - 2012
    Colorado School of Mines - Golden, CO
  • Bachelor's degree in Statistics
    2007 - 2010
    Colorado School of Mines - Golden, CO

Certifications

  • Project Management Certificate
    JANUARY 2020 - PRESENT
    Cornell University

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