<aside> 💡 The topics covered in this chapter apply to internal employees of Remote. That is, someone who is working for and at Remote. Changes to the compensation philosophy are made by the internal Compensation Committee, which consists of: CEO, Sr VP of Finance, Total Rewards Team and the VP of People.
Table of Contents:
Remote's Total Rewards mission is to ensure fair, unbiased compensation and equity pay along with competitive benefits in all locations in which we operate to ensure we attract and retain amazing talent globally.
We do not agree to or encourage cheap-labor practices and therefore we ensure to pay above in-location rates. We hope to inspire other companies to support global talent-hiring and bring local wealth to developing countries.
We foster an open and transparent environment at Remote and are always open to conversations from our Remoters regarding compensation.
We specifically refer to Total Rewards so as to include base and incentive pay, statutory benefits (in the locations we are able to offer this) and our global benefits and perks including working in a flexible remote-first organization.
How it started:
The People Team originally implemented a paid compensation data application called Kamsa. We have went through a process with the Total Rewards Team, management and Kamsa to level and benchmark every position at Remote and completed a fair pay and equity analysis.
Although Remote aims to be transparent about compensation benchmarking, we might not be able to do so publicly yet, considering we are paying for compensation data and our Vendor agreement may not allow this. We are working toward pay transparency in the future.
Remote currently does not offer global pay. Director levels and above are benchmarked at min. 80% of a Global US rate. For the rest of our population, we are using several factors when we make a compensation decision at hiring or during an increase/review process.
Interested to learn more about Kamsa as a compensation source, the size of their database and processes used to prepare benchmarking? 🤔