"Bitget Slashes Hiring Time by 38% with AI-Driven Recruitment"

Bitget, a leading cryptocurrency exchange and Web3 company, has revolutionized its recruitment process by integrating artificial intelligence (AI), resulting in a 38% reduction in hiring time. The company's AI-driven recruitment solution has not only streamlined talent acquisition but also improved candidate-job alignment, leading to increased workforce efficiency.

The implementation of AI in Bitget's recruitment process has yielded several key benefits. The average hiring time has been reduced by 38%, with resume screening efficiency improving by 76%. This automation has allowed hr specialists to focus on higher-level candidates, rather than manual filtering. Additionally, recruitment costs have dropped by 25% due to automated hiring workflows, and employee retention has improved by 15% as a result of better candidate-job fit, leading to a lower first-year attrition rate.

AI-driven candidate ranking and skill-job matching have increased hiring accuracy, lowering bias in recruitment decisions by 38%. Before adopting AI-driven hiring, bitget relied on manual candidate screening and external recruitment agencies, which made recruitment costly and time-consuming. The average hiring cycle lasted 48 days, with some technical positions taking up to 50 days to fill. High dependence on third-party agencies accounted for nearly 40% of total hiring costs, while internal HR teams processed up to 500 resumes per month, leading to operational inefficiencies.

To address these challenges, Bitget introduced an AI-powered recruitment system designed to streamline hiring by automating resume screening, optimizing candidate-job matching, and improving decision-making. The AI model was trained using historical hiring data, evaluating key indicators such as skill compatibility, previous performance, and cultural fit. Integrated with existing HR systems, the technology enabled rapid candidate ranking and selection while reducing human bias.

The results of this transformation have been significant. The average time to hire dropped by 38%, cutting recruitment cycles from 48 to 30 days. Resume screening efficiency improved by 76%, allowing HR specialists to focus on high-value candidates. Cost savings reached 25%, primarily due to reduced reliance on external agencies and the automation of administrative hiring processes. Employee retention improved by 15%, as better candidate-job alignment led to a decrease in first-year attrition. Additionally, AI-driven evaluations helped minimize unconscious bias in hiring decisions, resulting in a 38% improvement in hiring accuracy.

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