Interview brownie

What we built

A tool to differentiate you from the crowd during your next job interview by providing thoroughly researched talking points about the company/product without having to do the grunt work

Category Submission

Business

App Link

https://gbrownie-point-tx6wl.ondigitalocean.app/
https://gbrownie-point-tx6wl.ondigitalocean.app/admin

Screenshots

Description

Regardless of which side of the interviewing panel you are on - a candidate who does their research on the company/product stands out. Period.

They come across( rightly so ) as prepared, genuinely interested in the role / company as opposed to appearing for yet another interview

It could be the awareness of the company's strategy(public info ofcourse)
OR
the knowledge of the most liked/disliked features of the company's flagship offering.

These "brownie" points go a long way in the selection process.

This is something that I have experienced first hand and wanted to test the utility of a product which facilitates this for a candidate.

So ,

gauravdagde

coding-coffee-bike_rides-foodie

and I have created this tool to help you earn brownie points in your next interview, without having to put in the grunt work

The project comprises of 4 core flows, we will be detailing each stage further in another post:

1. User interface for prospective candidates to

   • Understand the proposition of the product
   • Generate the report for themselves by filling crucial information such as target company, role ( <1 minute fill time )

2. Report generation -
   Report containing 2 broad sections:

-End user understanding
Familiarize themselves with the biggest positive and negative feedback points from the company's end consumer reviews, sorted by their sentiment
*Only google playstore for now, we will extend it to app store soon
-Social / PR synthesis
Swift through the relevant quotes / mentions from the company without having to manually go though every single article.
If it's on google, it will be in their report.

1. Admin panel
   Internal tool for the domain / interview expert to QC the system generated report

2. Personalized mail generation and sender
   Populate the contents using text, visualization outputs of plotly and trigger the mail upon the QC go ahead from the reviewer

Link to Source Code

https://github.com/gauravdagde/brownie_point

Permissive License

Common clauses

Background

This project came about from :

1. Our personal experience interviewing for roles
2. Our experience of taking interviews for roles in our teams
3. Soundboarding the use-case with our friends and colleagues

How we built it

We will write a series of posts breaking down the steps in each flow, here is the overview for now:

1. User interface
   Used an existing template from bootstrap4 to embed a typeform. We are using web-hooks to share data from typeform into our system which are logged as interview request on our django admin panel for perusal by the manual reviewer

2. Report generation

The app fetches data for the target company name via google news APIs and scraping the reviews off of playstore, which then goes through a sequence of data wrangling flows and finally for visualizations.

We are using the google search API to scrape articles, headlines and URLs. We then use BS4 to scrap the URLs.

Tag architecture: We have written a tagging schema which categorizes the articles from google news API basis the keywords present in them.

Utilized doctl from the digital ocean platform to deploy the django app after setting up the postgres connection using DO database.

We have used pandas for data manipulation, nltk and sklearn for sentiment analysis, plotly for visualizations

1. Admin panel
   Built over Django, the report can then be vetted and edited in real time by a human to make it the most effective for the user. The idea is to monetize this separately as an interview / domain expert review. We have the baseline tech built for this, the UX will be improved as we go along

2. Personalized mail generator and sender
   The output from the report is then populated and sent using the gmail client

Additional Resources/Info

https://github.com/kotartemiy/pygooglenews
https://pypi.org/project/google-play-scraper/