Projects

Excel Add-in for the Compensation Team
As I created macros and Python scripts to automate our day-to-day, I created a toolbar to make all the tools easier to find and access. When I released a new version of the code to correct issues or to add new features to it, it would automatically update the new version for the rest of the team. The toolbar was coded in VBA and Python, and the user interface was done with XML.

Data Cleaning for Compensation Survey data from a zip file and multiple sheets to Workday consumable format
One of our compensation survey vendors give us data on a challenging format. It comes with a zip file full of folders with many unnecessary Excel files and some necessary files. This tool unzips everything, sorts what is important versus what’s not, consolidates all the files, rearranges columns, extracts information from other tabs, and puts together a bigger data frame with the information we need to upload into our system. Coded in Python.

Uploading data from Excel file to a table on a web page
I started with this project as a teacher who had to manually type the grades of all my students manually to a web page on our learning management system. Years later I saw an opportunity to use it again when I saw myself in front of a huge table asking about attrition data in a Qualtrics survey from one of our survey vendors. Coded in VBA.

Midpoint prediction tool for ad-hoc pricing (concepts and code samples)
One of the processes that used to take a great deal of time daily from our work schedules in the Compensation team was pricing jobs whenever a manager, an HR Business Partner, or a Talent Acquisition partner logged a case to have a job activated for a new location (we're in many countries). The first time I priced a job I had to open several different Excel reports, filter to get the data I needed, populate a template, and then start thinking and making my pricing decisions. Each job would take us about 20 minutes and the reports were massive, which would add to the time while Excel would freeze multiple times during the process. I developed a tool using VBA, Python, and XML for the graphical user interface to automate this, and as a result, we have a more streamlined process that takes less than 2 minutes per job.

Data cleaning and consolidation of data from several survey vendors into one market composite
Data that comes from our system is not ready to be used. This tool consolidates three huge files into one file that is ready for the compensation team to use to get the market data from. Saved 99.5% of the time we used to spend on this process. Saved 97% of the storage space, coming from 300 MB of files to only 10MB.

Outlier detection for compensation range progression by country and levels
Auditing the current compensation structure to spot possible errors in compensation range progression to see if anything is too high or too low. We added this as a systematic audit for our annual market study to improve the quality and consistency of our pay ranges. Coded in Python with Jupyter Notebooks.

Cost of labor comparison by location
Using currency exchange rates, inflation data, and other internal considerations, merging all this data into one place and analyzing to compare costs between different countries to difference in cost of hiring for different locations. Coded in Python with Jupyter Notebooks.

Compa-Ratio Catch-up Tool
This tool iterates through an employee roster to spend budget on the lowest paid employees through the compa-ratio catch-up method. Coded in VBA, runs in Excel through a form.

Determine FLSA Status With Categorical Data
Using predictive analytics based on existing jobs within our job structure, these models will predict FLSA status for overtime eligibility. Coded in Python with Jupyter Notebooks

Determine FLSA Status With Natural Language Processing
Using predictive analytics based on job descriptions and natural language processing, these models will predict FLSA status for overtime eligibility, this time with 4 models instead of 2. Coded in Python with Jupyter Notebooks

Pay Equity Analysis
Performing pay equity analysis using Python on a Jupyter Notebook.

Pricing Preview Form
Designed a form using MS-Forms so recruiters and HR Business partners who are considering activating jobs in new locations can request to know pay ranges without going through the whole 2-weeks long process to get the job created and priced in our HR information system. Automated the notification to the Compensation point of contact to know of the new job that needed pricing. Automated the feedback email for the compensation point of contact to reply with the results of the pricing. The process came from 2 weeks to max 2 business days. Used Typescript, Power Automate, and VBA for Excel.