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The Data Science Team focuses on building models that leverage Payscale's five core compensation datasets to provide accurate, high coverage estimates of compensation ranges for jobs across industries and the globe. We use modeling techniques such as Bayesian statistics (regression, hierarchical modeling, and transfer learning), deep learning (NLP, LLMs, and embeddings), and recommendation systems to model how different jobs are related to each other in order to produce good compensation range predictions. We do a mix of development on well-defined projects and greenfield innovation. We build internal tools (APIs and interactive demos (using e.g. Streamlit) to exhibit our work. We value teamwork, learning together, maintainability, documentation, and giving clear presentations about our work to non-ML stakeholders. We are generalist problem solvers-we use (or learn!) the best tool for the problem. Our team works closely with compensation domain experts to help define the problems, identify and validate our assumptions, and evaluate our predictions. We are supported by a separate Data Engineering Team that helps turn our models into production APIs for use in products across Payscale's portfolio.
