In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments' success at the level of a single job placement, and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning (ML) model, which in this case is obtained by applying the Variable-Order Bayesian Network (VOBN) model to the recruitment data. Specifically, we used a uniquely large dataset that contains recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN model can provide both high accuracy and interpretability insights to HR professionals. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods.

We demonstrate that it is feasible to predict the successful placement of a candidate in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two.

在本文中，我们提出了一个全面的分析框架，可以将其用作现实环境中人力资源招聘人员的决策支持工具，以改善招聘和安置决策。拟议的框架分为两个主要阶段：在单个工作岗位级别上针对招聘成功的本地预测方案，以及考虑了多级考虑因素的数学模型，为组织提供了全局招聘优化方案。在第一阶段，所提出的预测方法的关键属性是机器学习（ML）模型的可解释性，在这种情况下，是通过将可变阶贝叶斯网络（VOBN）模型应用于招聘数据而获得的。特别，我们使用了一个独特的大型数据集，其中包含十年来数十万名员工的招聘记录，并代表了广泛的异类人群。我们的分析表明，VOBN模型可以为人力资源专业人员提供高精度和可解释性的见解。而且，我们表明，使用可解释的VOBN可以导致出乎意料的，有时甚至违反直觉的见解，而依赖常规方法的招聘人员可能会忽略这些见解。

我们证明，在聘用前阶段预测候选人在特定位置的成功放置并利用预测来设计全局优化模型是可行的。我们的结果表明，与实际的招聘决定相比，尽管两者之间存在内在的取舍，但设计的框架能够提供平衡的招聘计划，同时提高多样性和招聘成功率。