Recent Projects

A World-Leading Beverage Company

 

In 2021, sub-contracted in service to a major beverage company that required  demand forecasting models to incorporate the impact of COVID and macroeconomic factors in client’s monthly demand planning process.

My part was to:

  • Write Python code to combine COVID-informed demand predictions with other competing forecasts or comparison.

  • Deliver those data to manager-specific spreadsheets that allowed managers to override demand predictions if needed.

  • Create and execute in-person training, videos and documents that instructed managers on:

    • the monthly demand planning process; 

    • how and when in the planning process to access the spreadsheets;

    • how to override forecasts within the spreadsheets and submit them to the demand planning system.

A World-Leading Retailer

 

Currently sub-contracting in service to a major retail client, also requiring demand forecasting models to better reflect the current COVID and macroeconomic environment in their demand planning processes.

My duties here include:

  • Forecast data visualization:

    • "Big data" wrangling of massive demand forecast datasets for delivery into visualization software;

    • Creating visualizations that allow business users to consume:

      • Forecast model accuracy, to instill confidence in the forecast models;

      • Short and long-range units and revenue forecasts;

      • The impact of demand explanatory variables.

  • Baseline price forecast model development, enabling units forecasts to be converted to revenue.

  • Sourcing and updating of demand model explanatory variables.

Sharing Knowledge

 

In 2021 I volunteered to develop and teach a course called Introduction to Data Science for the University of Wisconsin - Milwaukee's College for Kids & Teens program. This two-week summer course, which I look forward to teaching again in 2022, is offered to students entering grades 9 through 12, and exposes them to:

  • Installing and configuring Python, R and Jupyter Notebook on their Windows, Mac or Chromebook machine;

  • Data analysis programming in Python and R;

  • Exploring data using visualizations (scatter plots, line charts, etc.) in Python and R;

  • "Big Data" and "Cloud" environments used in data science;

  • The basics of predictive model building (regression).

For more information on this course, please see https://uwm.edu/sce/courses/introduction-to-data-science/.