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

Data Scientist and Developer in Denver, CO, United States

Member since March 31, 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




Denver, CO, United States



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%.


  • Data Scientist

    2020 - PRESENT
    The Energy Authority
    • Developed an algorithm to detect broken and defective meters based on usage patterns, saving our clients millions of dollars in lost revenue each year.
    • Successfully managed multiple projects across multiple clients, from requirements gathering through final product delivery.
    • Implemented deep learning neural nets to predict customer behavior and automatically identify specific usage characteristics, which saved our clients millions in maintenance costs while increasing customer satisfaction.
    Technologies: Deep Neural Networks, Deep Learning, Data Modeling, Customer Data, Time Series Analysis, Sensor Data, Clients, Project Management, Data Science, Azure DevOps, SQL, Microsoft Power BI, Microsoft Azure, Databricks, Python 3, R
  • 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: Scala, Microsoft Excel, 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


  • High Frequency Trading Strategy

    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

    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

    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

    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.


  • Languages

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

    R Studio, Microsoft Excel, MATLAB, LaTeX, Microsoft Power BI, Azure DevOps Services
  • Paradigms

    Data Science, Azure DevOps, ETL
  • Other

    Data Analysis, Statistical Analysis, Data Visualization, Data Analytics, Statistics, Commodity Trading & Risk Management (CTRM), Profit & Loss (P&L), Machine Learning, Financial Data Analytics, Minitab, Deep Learning, Microsoft Azure, Clients, Sensor Data, Time Series Analysis, Customer Data, Data Modeling, Deep Neural Networks
  • Frameworks

    RStudio Shiny
  • Platforms

    Databricks, Jupyter Notebook, Linux
  • Storage

  • Industry Expertise

    Project Management


  • 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


  • Certified Analytics Professional (CAP)
    MAY 2020 - MAY 2023
  • Project Management Certificate
    Cornell University

To view more profiles

Join Toptal
Share it with others