[Remote] Data Scientist - Reinforcement Learning

Note: The job is a remote job and is open to candidates in USA. EXL is a company focused on digital solutions, and they are seeking a Data Scientist specialized in Reinforcement Learning. The role involves designing and developing models to optimize collections strategies and customer engagement, while collaborating with business stakeholders to deliver scalable AI/ML solutions.

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:
• Q-Learning
• Deep Q Networks (DQN)
• Policy Gradient Methods
• Contextual Bandits
• 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

Skills

• Experience (In Years): 3-6
• Strong experience in Reinforcement Learning and sequential decision-making systems
• Hands-on expertise with: Reinforcement Learning algorithms (Q-Learning, DQN, PPO, Bandits, etc.)
• Markov Decision Processes (MDP)
• Experience in collections, credit risk, customer analytics, or financial services domains
• Familiarity with: Deep Learning frameworks (TensorFlow, PyTorch)
• MLOps and CI/CD workflows
• Real-time decision systems
• Cloud platforms such as AWS, Azure, or GCP

Company Overview

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