Data Scientist - Reinforcement Learning

Key Responsibilities
•    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. 
 

Preferred / Good-to-Have Skill
•    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

Must-Have Qualifications
•    Strong experience in Reinforcement Learning and sequential decision-making systems. 
•    Hands-on expertise with: 
o    Reinforcement Learning algorithms (Q-Learning, DQN, PPO, Bandits, etc.) 
o    Markov Decision Processes (MDP)