Data Scientist - Reinforcement Learning

• Design and develop Reinforcement Learning models to optimize collections strategies, customer treatment paths, and recovery outcomes.
• Build adaptive decisioning systems using techniques such as:

o Q-Learning o Deep Q Networks (DQN) o Policy Gradient Methods o Contextual Bandits o Markov Decision Processes (MDP)

• Develop sequential and behavioral models for customer engagement, repayment prediction, and collections prioritization.
• Apply stochastic modeling and probabilistic methods to optimize dynamic treatment strategies under uncertainty.
• Collaborate with business stakeholders to translate collections and risk management problems into scalable AI/ML solutions.
• Build and maintain machine learning pipelines in Databricks or similar distributed computing environments.
• Conduct experimentation, simulation, and offline policy evaluation to validate RL strategies before deployment.

• Experience in collections, credit risk, customer analytics, or financial services domains.
• Familiarity with:

o Deep Learning frameworks (TensorFlow, PyTorch) o MLOps and CI/CD workflows o Real-time decision systems o Cloud platforms such as AWS, Azure, or GCP Qualifications: Must-Have Qualifications

• Strong experience in Reinforcement Learning and sequential decision-making systems.