- The application deadline has been extended until December 18, 2022.
- Vistex is an SAP software solutions extensions partner serving businesses of all sizes worldwide across a spectrum of industries by managing their master data, contracts, pricing, rebates, and incentive programs. Our solutions provide unprecedented visibility into the breadth and depth of go-to-market programs and enable businesses with insightful information that drive revenue, control costs, and increase margins. Our deep industry expertise coupled with in-house implementation, delivery, and training services makes Vistex a full solutions resource and consultancy.
Center for Data Science Funded Project Opportunity
The VISTEX Funded Project Summary:
The DePaul Center for Data Science (CDS) is pleased to announce a partnership with Vistex, an industry leader in go-to market solutions. Vistex provides companies with innovative solutions to manage the full life cycle of their programs through strategy, software, implementation, execution, and analytics.
As part of this partnership, Vistex is sponsoring a number of data science projects to provide students with real world experience and a deep understanding of complex data science challenges.
The first project is titled “Agent based modeling of price guidance”. A team of three data science students will work on the data science project under the supervision of Prof. Ilyas Ustun. This project can be used to fulfill the capstone requirement for the MS in Data Science degree program.
If you are interested in participating in this opportunity, please apply using the form below. More information about the project and the selection criteria can be found below.
Price guidance is a common concept used in Business-to-Business pricing. It consists of a couple of reference prices like a floor price, a target price, and an expert price that are used to provide guidance to the sales agent who is trying to respond to a request by a customer for a product or service. The reference prices are calculated based on the outcomes of previous quotes for similar products to similar customers.
Imagine that this quoting process is automated. The customer asks for a product, then an algorithm representing the supplier responds with a quote and finally the customer either rejects or accepts, and that’s the end of the quoting process. What would be a good algorithm for the supplier? How to assess using repeated simulations of this process how this algorithm performs?
Vistex designs a repeated transaction with one supplier and a series of customers with the same willingness-to-pay distribution. The supplier does not know the willingness-to-pay distribution. The supplier can make one price quote per request and based on that the customer will either accept or reject. Vistex provides a simple baseline model for the supplier as a reference point. Possible tasks for the intern:
(i) Determine optimal estimation and optimization method
(ii) Determine impact of competition, and let suppliers compete with different methods
(iii) Determine impact of segmentation, and assess value of cross segment information
The students must be available to work for 10 weeks during Winter quarter for 10 hours per week at a pay of $16 per hour. Start date will be January 2, 2023.
- Strong analytical skills
- Experience with Python programming
- Experience with statistical analysis and machine learning techniques
- Experience with optimization methods is a plus
- Interest in business processes and go-to-market strategies is a plus
- Student must be enrolled in the MS in Data Science degree and have completed all the foundation courses
- The students must be available to work for 10 weeks during Winter quarter for 10 hours per week
- Students must have a minimum GPA of 3.0.
The application should include:
- Informal transcript
- Recent class project student worked on
- A data science project done outside of class (if applicable)
- A 3-minute video recording of you presenting your favorite data science project
- More details can be found in the application link below.