Required Skills/Experience: Must possess 5 years of experience with Master's or 7 years of experience with Bachelor's with all of the following: developing and deploying machine learning solutions within regulated financial services environments; developing analytical use cases across credit risk, fraud prevention, or payments; developing and training machine learning models in large-scale and distributed data processing environments, using Python programming, SQL data querying, and performing end-to-end machine learning workflows; conducting model diagnostics and explainability analyses to assess model performance, stability, and drift; working with large structured datasets in enterprise and production data environments; and collaborating with business and technology stakeholders to translate requirements into analytical solutions in enterprise environments. Responsibilities include applying modern machine learning and artificial intelligence techniques-such as data mining, data modeling, natural language processing, computer vision, and other emerging methods-to extract insights from large structured and unstructured datasets; collaborating with internal partners and external stakeholders to gather requirements, evaluate third-party data sources and solutions, and integrate AI/ML capabilities into existing systems and business processes; and leading the development, support, and maintenance of end-to-end, production-grade AI/ML pipelines encompassing data preparation, model training, validation, deployment, monitoring, and retraining.