Allegis Global Solutions is founded on a culture that is passionate about transforming the way the world acquires talent by delivering client-focused solutions that make a difference for businesses worldwide. From refining how you manage your contingent workforce to strengthening your employer brand to recruit top talent, our integrated talent solutions drive the business results you need. As an industry leader, we draw upon decades of experience to design innovative tools, products and processes. We develop competitive practices that position organizations for growth and we deliver the insight needed to succeed in today's global marketplace. The Data Governance function is responsible for supporting the MSP implementation process by gathering incumbent worker data, analyzing gaps, verifying information and preparing upload templates that will be loaded onto the Vendor Management System (VMS) prior to the launch of the account and handover to operations.

In machine learning, crowdsourcing is an economical way to label a large amount of data. However, the noise in the produced labels may deteriorate the accuracy of any classification method applied to the labelled data. We propose an enhanced nearest neighbor classifier (ENN) to overcome this issue. Two algorithms are developed to estimate the worker quality (which is often unknown in practice): one is to construct the estimate based on the denoised worker labels by applying the $k$NN classifier to the expert data; the other is an iterative algorithm that works even without access to the expert data. Other than strong numerical evidence, our proposed methods are proven to achieve the same regret as its oracle version based on high-quality expert data. As a technical by-product, a lower bound on the sample size assigned to each worker to reach the optimal convergence rate of regret is derived.