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Past work

Here is a non-exhaustive overview of work I've done with ML & AI.

A Disclaimer of Sorts

Each of these projects was a unique problem based on the business, data availablity & amount, and other constraints.

We can operate within most constraints but not all.

Additional note, examples are framed in a business context. Most projects were done end-to-end -> data prep & cleaning, problem definition, model training, evaluation, deployment & monitoring.

Pricing & Inventory

Promotion Pricing Optimization:

  • What: Model that predicted the item(s) most receptive to promotion + the optimal discount for sales and margin dollars.
  • Value: Drives better return on promotionala dollars, increases sales or margin dollars.

Inventory Optimization:

  • What: Suggest optimal order volume to meet demand of promoted items.
  • Value: Minimize lost sales from out of stock items and lost inventory from over ordering.

Marketing & Sales

Match acquisition cost and customer lifetime value

  • What: A Google adwords bidding model that identified amount to bid that maximized customer acquisition at acceptable customer lifetime value threshold.
  • Value: Allowed business a controlled way to grow customers without getting "upside down" on return on ad spend.

Lead Scoring & Allocation:

  • What: Identify prospects with highest conversion likelihood and paired prospect with "best fit" sales rep.
  • Value: Doubled sales conversion at end of three month "calibration" period.

General

Text to SQL:

  • What: User asks natural question like "what was my top selling product last week?" to a SQL query (and data output) tool.
  • Value: Enables business to be better informed without relying on technical resources for answers.

Impact of user action:

  • What: User books a service today, what value does this create over the next three months relative to a similar user who doesn't book a service today?
  • Value: Improves understanding of quality of service provided and enables better planning.