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.
Developed an app that harnesses the power of ChatGPT to make AI accessible to real estate professionals. This app not only uses ChatGPT to create content but integrates data from other sources to provide richer context.
Created a business to sell data science as a subscription service. This business model is a new way to access and interact with senior talent.
Took an idea from conception through MVP and into production. Marketed and sold the product through relevant industry avenues.
Developed and deployed a model to predict a customers income based on initial credit bureau data. By making the prediction before requiring additional information we reduced friction and churn throughout the underwriting process.
Built cashflow models and forecasting tools to accurately predict customer repayments.
Led the transition to kubernetes from a data science perspective. Orchestrating models into model as code and providing configuration assets for deployment in K8s clusters.
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.
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.
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.
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%.
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%.
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.
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.
Education
Master's Degree in Applied Mathematics and Statistics
