[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
Company H1B Sponsorship