Senior Data Scientist - Reinforcement Learning

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    "ShortDescriptionStr": "Senior Data Scientist - Reinforcement Learning - Philadelphia\n\nWe are seeking a highly skilled Data Scientist with expertise in Reinforcement Learning (RL), optimization, and advanced analytics to support Collections strategy initiatives for a major client.\nThis role focuses on developing intelligent decisioning systems and adaptive collection strategies using reinforcement learning, sequential modeling, and machine learning techniques. The ideal candidate should have strong experience in building AI-driven systems that optimize customer engagement, payment recovery, treatment allocation, and collections workflows across large-scale environments.\nThe role requires a strong foundation in machine learning, stochastic processes, experimentation, and scalable model deployment using modern cloud and big data platforms.",
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    "ExternalDescriptionStr": "<p><span><strong>Key Responsibilities</strong></span></p>\n<ul>\n <li><span>Design and develop Reinforcement Learning models to optimize collections strategies, customer treatment paths, and recovery outcomes.&nbsp;</span></li>\n <li><span>Build adaptive decisioning systems using techniques such as:&nbsp;</span>\n  <ul>\n   <li><span>Q-Learning &nbsp;</span></li>\n   <li><span>Deep Q Networks (DQN)&nbsp;</span></li>\n   <li><span>Policy Gradient Methods&nbsp;</span></li>\n   <li><span>Contextual Bandits&nbsp;</span></li>\n   <li><span>Markov Decision Processes (MDP)&nbsp;</span></li>\n  </ul></li>\n <li><span>Develop sequential and behavioral models for customer engagement, repayment prediction, and collections prioritization.&nbsp;</span></li>\n <li><span>Apply stochastic modeling and probabilistic methods to optimize dynamic treatment strategies under uncertainty.&nbsp;</span></li>\n <li><span>Collaborate with business stakeholders to translate collections and risk management problems into scalable AI/ML solutions.&nbsp;</span></li>\n <li><span>Build and maintain machine learning pipelines in Databricks or similar distributed computing environments.&nbsp;</span></li>\n <li><span>Conduct experimentation, simulation, and offline policy evaluation to validate RL strategies before deployment.&nbsp;</span></li>\n <li><span>Work with large-scale structured and unstructured datasets to derive actionable insights and improve operational performance.&nbsp;</span></li>\n <li><span>Partner with engineering and MLOps teams to deploy and monitor production-grade ML/RL models.&nbsp;</span></li>\n <li><span>Mentor junior data scientists and promote best practices in modeling, experimentation, and AI governance.</span></li>\n</ul>",
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