A.Team
Creating opportunities for freelance talent on a members only platform.
Recognitions
Award
Fast Company
World Changing Ideas
Award
Inc. Power Player One
Leader in AI & Data
Services
Design strategy
User research
Market research
Data analysis
Interaction design
Interface design
Design system
Deliverables
Design roadmap
Research report
Product spec
Product tracking plan
6 products brought to market
Team involved
Eliot Raymond
Product Manager
Luis Montoya
Engineer
Anibal Vergara
Engineer
Peter Grobler
Engineer
Naveed Ahmad
Engineer
Introduction
In 2022, I joined A.Team, a members only network that helps top, vetted, freelance talent find work in the tech space.
Now, getting a job is not easy. For most people, it’s a stressful, challenging process. Especially when you’re looking for freelance work and don’t know where your next payment will come from. But for a small percentage of people, it’s a walk in the park. During my first year at A.Team, my job was to understand the challenges of people struggling to find work, understand what separates them from the top performers and build tools that will help them level the playing field and increase their chances.
Uncovering the challenges
To understand and begin addressing our members' issues, I analyzed the platform data and conducted interviews with network members to gain firsthand insights. This research revealed two categories of problems: platform-specific issues and challenges within the global hiring industry.
Globally, applicants struggle to represent their experience effectively, failing to highlight relevant details and contributions, making it hard to align with the hiring company needs. The hiring process lacks transparency, providing little feedback to rejected applicants. This sometimes leads clients to hire internally due to perceived candidate shortages.
On our platform, a broken onboarding process deterred a lot of talented individuals, and those who completed it often abandoned their profiles due to its length. Our selection team didn't trust the automatic recommendation system, preferring manual reviews and established relationships, creating bias and limiting opportunities for new talent. Additionally, the absence of status updates and an off-platform, slow interview process mirrored industry-wide issues, contributing to lost deals and delayed hiring.
Designing the solution
When developing the platform strategy, I focused on addressing immediate member issues and innovating to enhance job-finding tools. I categorized the issues by complexity and impact, prioritizing those with high impact and low complexity for quick wins. This boosted morale and re-engaged users on the verge of leaving. Afterward, we tackled more complex issues to reduce time-to-hire and secure more deals.
Feedback and transparency
We first introduced project statuses, that provided process transparency and applicants knew whether the company is actively hiring, or they're already reviewing candidates. We also started showing job relevance to candidates, so they knew if they had experience that qualifies them for the role.
These two signals helped us increase engagement with jobs, since people now knew exactly which projects they should apply to and which ones would be a waste of time and the quality of applicants became significantly higher.
Additionally, we introduced application statuses, providing automated feedback at each stage, and later scaled this to include client-specific rejection reasons.
This helped boost the satisfaction of applicants, who now knew exactly where they are in the process, as well as how to improve next time.
Improving our members chances
Next, we simplified the onboarding from over 30 steps to just two. We've now required the bare minimum that allowed us to understand the applicant's experience and pre-fill their new profile.
We then used AI to enhance their profiles by suggesting improvements based on their LinkedIn and resumes, leading to higher quality profiles and better chances of selection and easier evaluation of their experience both by our internal teams as well as clients.
Super powering talent selection
Since we improved profile data, we had a lot more to go on when automatically reviewing data. We've tested the system rigorously and our teams had a lot more confidence in it.
Considering the improvement, we could now automate the recommendation and selection processes to reduce bias and increase opportunities for more builders. We've seen a 65x jump in the number of different members who have been considered for a project, within the first month alone.
We also introduced AI-powered application guidance, offering live feedback and scoring to help applicants align their profiles with project requirements, improving their chances of success. The application guidance significantly helped members understand what's being asked of them, helping them provide more relevant data and stand out when applying.
Outcome
Before introducing the AI profile suggestions, only 27% of our network had high quality profiles that were good enough to be presented to clients. We’ve raised that number to 82% within 3 months of the launch.
The new selection system opened the platform up to a record number of 89% of our network. Meaning that 59% more users have interacted with and applied to new projects.
Application statuses lead to a 49% jump in engagement on projects. Applications improved with the application guidance and we’ve improved the quality of talent that was presented to clients with automatic candidate selection, which lead to over 92% satisfaction rate with the initial hire.
All these initiatives lead to a significant increase of opportunities for our members and increased the number active members on projects by 24x, which directly boosted the company revenue by 70%.
