PharmaceuticalsAI-powered recruitment tool for CV analysis and ranking
About the Client
Our client specializes in matching top-tier talent with positions in the pharmaceutical, biotech, and medtech sectors. Their mission is to streamline the recruitment process and discover the industry's top professionals. Our role at HW.Tech was to help the client build efficient business workflows to reduce the time, effort, and costs associated with manual candidate screening. We transformed their approach to talent acquisition by leveraging AI-driven technologies.
Problem Statement
In any industry, the recruitment process can be accelerated and made more efficient, minimizing the need for manual screening and evaluation of candidate profiles. For example, even with a talent pool of 7,000 individuals on LinkedIn, manually reviewing profiles for a single recruitment request can take up to 160 hours.
Recruiting in the pharmaceutical sector is even trickier, as jobs here require scientific, regulatory, and business expertise. For our client, finding an ideal candidate for a single position typically takes about a month and involves the efforts of three recruiters on average.
Solution
HW.Tech started collaborating with the client by developing a Proof of Concept (PoC) for an automated recruitment platform that integrates AI-driven candidate ranking and an interactive dashboard. It streamlines candidate profile analysis, enabling the easy identification of candidates matching the characteristics of the benchmark profile. The process follows these steps:
- Users initiate the process by uploading a CV of a benchmark candidate in PDF or entering their LinkedIn (LI) profile and outlining specific role criteria.
- The AI model considers benchmark candidate criteria to construct a similarity model based on extracted information, allowing users to prioritize criteria.
- Users upload CVs or enter LI profiles for analysis against the benchmark profile's traits.
- The system identifies the 'closest peer' candidate and provides the reasoning behind the ranking, offering users insights into the selection process.
- Through the dashboard, users can either accept or decline rankings, offering reasons through free text input.
- The system recalculates rankings based on user feedback, using an iterative loop approach, until the candidate is approved.Once the users approve the ranking results, they can view the list of candidates ranked by their similarity score. They also can download the ranking results as a PDF report.
Feature Engineering
At HW.Tech, we deeply value our clients' time and resources. To bolster PoC development, our engineering team introduced the Unified AI Adapter (UAIA). This versatile platform serves as a flexible constructor to build sophisticated AI solutions and ensure their seamless integration into existing business systems.
- UAIA empowers the design and management of workflows of any complexity.
- We can easily integrate our solutions with external services or the client's internal systems through APIs.
- In our client’s specific case, we integrated the tool with the existing CRM and HRM systems, automating candidate information input.
- AI engine also integrates with this client's LinkedIn Recruiter Tool, providing a cohesive recruitment workflow.
- UAIA optimizes AI model training processes, enhancing the platform's adaptability and capabilities.
Result
Our solution cuts the screening time by 10x per candidate, easily managing a large number of CVs and profiles. With this enhancement, recruiters can screen a wider talent pool more effectively. Plus, with advanced AI and machine learning algorithms in the mix, the recruitment approach becomes more pragmatic, and candidates are ranked against predefined benchmarks much more accurately.
- Objective evaluation. Data-driven insights minimize subjective perception in candidate evaluation, eliminating human biases in the screening process.
- Lower cost. Automating initial screening processes significantly reduces staffing and resource expenditures.
- Learning algorithms. Continuous learning from user feedback and industry trends makes candidate recommendations more accurate and relevant.
- Scalability. The HW.Tech’s UAIA framework ensures a scalable design, allowing the client to handle higher data volumes and growing demands without compromising performance.
Next Steps
Our modular system is flexible enough to accommodate extra functionalities the client might request. This could involve features like a chatbot for candidate inquiries or an automated LI search for quicker candidate sourcing. One of the milestones on our roadmap is developing a mobile app to empower candidates, making it easy for them to upload their CVs.
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