The Problem

Hiring the right talent is crucial for company success, but today's hiring process faces major challenges. The internet and AI have made job postings more accessible than ever, leading to a flood of applications for each position. Hiring managers now face the daunting task of sorting through hundreds or thousands of applicants, creating significant noise in the hiring process. This volume makes it increasingly difficult to identify top talent efficiently.

Compounding this issue, companies often rely on hiring methods that poorly predict job performance. Qualitative interviews, though widely used, frequently fail to accurately assess how a candidate will perform in the actual role. This disconnect between interview performance and job success leads to suboptimal hiring decisions and increased turnover.

To make matters worse, AI has rendered most traditional interview processes obsolete. Recently, an undergrad student at Columbia University named Chungin "Roy" Lee created Interview Coder, an "invisible" AI tool that helps candidates cheat on technical coding interviews. The tool can take screenshots of interview problems without detection and provide real-time AI-generated solutions. Lee claimed to have used it successfully to receive internship offers from Amazon, Meta, TikTok, and Capital One.

Research consistently shows that work samples are the best predictors of job performance. This finding has spurred a rise in project-based interviews, which offer several clear benefits:

They provide better insights into a candidate's actual skills and capabilities.
Candidates that use AI effectively will stand out from those that don't, as projects often have no right or wrong answer, just varying degrees of quality.
They reduce bias by focusing on work quality rather than subjective factors.
They can attract a more diverse pool of high-quality candidates who might be overlooked in traditional processes.

However, project-based interviews come with a significant drawback: they're expensive and time-consuming to implement. They require substantial time from hiring managers to create effective projects and from team members to thoroughly review submissions. A single project review can take anywhere from 30 minutes to several hours, a cost that quickly adds up, especially for companies dealing with a large volume of applicants.

This situation creates a tension between the desire for better hiring practices and the need to manage costs effectively. Many companies attempt to resolve this by narrowing their applicant pool early in the process. While this approach reduces costs, it also means potentially missing out on exceptional candidates who might not shine on paper but could excel in a project-based assessment. Additionally, this often leads to more bias, relying on heuristics and resume screens. For example, when Yelp moved its coding project earlier in the hiring process, they saw a significant increase in high-quality female candidates who had previously been eliminated during initial resume screenings.

Some organizations have turned to automated tools to review projects, but these solutions have limitations. They typically only work well for assignments with predetermined, fixed answers. When it comes to evaluating open-ended problems - which are often crucial in today's dynamic work environments - these tools fall short.

"Companies find themselves caught between two suboptimal choices: they can either use effective but expensive hiring methods, or opt for cheaper methods that don't accurately predict job performance."

The hiring world urgently needs a solution that offers the benefits of project-based interviews without the associated high costs and resource demands.

Solution: StackedHR

We've identified a significant opportunity: applying ordinal peer review to the recruitment process. This application of the proven technology directly addresses the challenges outlined in our problem statement, particularly in scaling project-based interviews.

StackedHR hopes to leverage this algorithm to eliminate the trade-off between quality and cost in hiring. The idea to build a platform that enables companies to implement project-based interviews at scale, without incurring prohibitive costs or sacrificing assessment quality.

How StackedHR Works

Project Definition: Create relevant projects, such as case studies, coding challenges, or financial models.
Evaluation Set Definition: Companies use our flexible system to define evaluation criteria and set relative importances / weights
Applicant Engagement: Candidates complete the project and then review 3-7 other submissions using the defined criteria, effectively creating a stack-ranked evaluation
Algorithmic Analysis: Our system processes these peer reviews, generating an overall candidate ranking and reliability scores for each reviewer
Efficient Final Review: Hiring teams start with a data-driven candidate ranking, streamlining their decision-making process

Key Benefits

Cost Reduction

By shifting the initial review process to applicants, we significantly reduce the time and resources companies spend on project evaluation.

Increased Scale

The marginal cost of each additional review approaches zero, allowing companies to dramatically expand their applicant pool or add more assessment rounds.

Enhanced Candidate Experience

Applicants gain the opportunity to demonstrate their skills directly and engage more deeply with the potential role.

Improved Performance Prediction

Project-based assessments, combined with peer evaluation skills, provide stronger indicators of job performance than traditional methods.

Bias Reduction

Focusing on work quality and anonymous peer assessment helps mitigate unconscious biases in the hiring process.

Wider Funnel

Companies may open the project to those who may not be part of their usual pipeline, allowing traditionally neglected but qualified candidates to break into an industry.

At the same time, we hope to empower applicants that may be overlooked in traditional hiring processes.

Candidates gain the opportunity to stand out based purely on the quality of their work and their ability to evaluate others' performances. This shift from traditional resume-based applications to skill-based assessments levels the playing field and opens doors for talent that might be overlooked in conventional hiring processes.

The Permanent Portfolio

Every project completed on StackedHR becomes part of your permanent professional portfolio. Unlike traditional take-home assignments that disappear after the hiring process, your work and peer evaluations become lasting credentials that showcase your abilities to future employers.

"Each project assessment becomes an opportunity to build your track record, regardless of the hiring outcome. Your demonstrated skills and peer evaluations tell the story of your capabilities to every future employer."

This creates a virtuous cycle where candidates are incentivized to do their best work, knowing it will have lasting value beyond the immediate application. For employers, it provides verified evidence of a candidate's abilities through consistent metrics and peer evaluations across multiple real-world projects.

The Future of Hiring

If you're interested in building out a more fair and meritocratic hiring pipeline, or have ideas for how we can improve the hiring process, please reach out to us at hello@stackedhr.ai

Ready to Transform Your Hiring?

Join the companies that are already revolutionizing their hiring process with StackedHR's peer-reviewed project assessments.