Brainvire Uses Machine Learning for Accurate Salary Prediction and Quickening the Hiring Process
About
The client is the largest entertainment and news publishing platform. Their platform provides local, national, and international news and other data. They are renowned in India for ground-breaking digital innovations and for supporting various publishing websites and portals. The company is quite popular, with 150 million page visitors on their site and a workforce of over 1,500 employees. Therefore, they realized the need for a solid HRMS platform, and our Microsoft team left no stone unturned to develop a great one.
Project Highlights
The new HRMS development aimed to provide salary prediction based on the previous compensations to enable hiring teams to decide the fair compensation and hire the right valuable resources. The team uploaded and stored a large pool of the company’s previous employees’ data. The machine learning implementation in the product utilized the data to predict the approximate salary the employee should offer. The large pool of data helped ensure result accuracy.
The Challenges
- Preferences and Security:Managing user preferences and data security in the HR prediction project.
- Salaries Prediction:Predicting employee salaries and hiring time using ML models.
- Salaries Prediction Communication:Communicating model predictions effectively to HR teams and stakeholders.
- Managing Model Accuracy:Managing model accuracy and bias when predicting employee salaries during hiring.
Tech Stack
CSS 3
JavaScript
Python
HTML
MySQL
Django
Azure DevOps
Azure Board
Result
Builds Trust Among Users
User authentication and authorization mechanisms implementation helps maintain compliance with data protection regulations and builds trust among users, making them more comfortable using the prediction services.Improved Resource Allocation and Decision-Making
Implementing machine learning models allowed the company to gain insights into appropriate employee salary levels based on their experience and anticipate the hiring timeline, aiding in better resource allocation and decision-making during the hiring process.Improved Communication
User-friendly interfaces improved communication and empowered the HR teams with actionable insights, facilitating more informed salary negotiations and staffing planning.Enhance HR Decision-Making
Improved machine learning model accuracy and reduced bias led to more reliable salary predictions and hiring time estimates. It enhanced HR teams’ decision-making capability and contributed to fairer compensation practices.
