Inspiration
As a first generation and low income college student, the world of personal finances is daunting and intimidating. This is the case for most FGLI students and recent graduates who are often the first in their families to experience things like internship applications, job offer consideration, and tech industry salary negotiation. When it comes to topics such as this, FGLI students and recent graduates are forced to navigate without guidance from a trusted person.
Of course, everyone struggles with things like these and we wanted to build something that could work for everyone, but the populations we wanted to target are first generation and/or low-income college students and recent graduates as they earn less than their peers in higher economic classes after graduation . This is specially concerning for female FGLI students as women have concerningly low rates of negotiating salaries, with 60% saying they have never done so
What it does
We created a web app that allows anyone to upload a PDF of an entry-level tech job offer, scans the document for the annual salary and job position offered, and compares the base salary to similar positions held by entry-level (4< years of experience) employees, then generates an email template for negotiating a higher salary if it is lower than the mean for that position.
How we built it
We used Python libraries nltk, pdfminer, pandas, and requests to first extract text from the PDF, then tokenize the words and sentences, then use those tokens to parse through the information and extract salary and job information. We also used levels.fyi for getting a salary data set that was specially focused on the technology industry and used pandas to analyze it.
Challenges we ran into
- Figuring out what technologies we would use
- Connecting the back-end and the front-end
- Getting the tokenizer to work
- Figuring out the best way to extract text from a PDF
- Finding usable salary data that was relevant to our interests
Accomplishments that we're proud of
- This is our first hackathon project!!
- Getting all of the front-end done
- Effectively using the natural language processing toolkit as well as pandas data analysis and manipulation tool even though it was the first time seeing them,
What we learned
- How to use pandas and natural language processing capabilities and data analysis techniques.
- How to get set up with flask
What's next for JobPanda
We hope to incorporate more back-end features, as well as incorporate location data and other job positions for a more personalized email.
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