Data Scientist - Reinforcement Learning

EXL • United States,

7h

About The Position

This role focuses on designing and developing Reinforcement Learning (RL) models to optimize various aspects of collections strategies, customer treatment paths, and recovery outcomes. The position involves building adaptive decisioning systems using advanced RL techniques and applying stochastic modeling to optimize dynamic treatment strategies under uncertainty. Collaboration with business stakeholders to translate problems into AI/ML solutions and building machine learning pipelines are key aspects of this role. The scientist will also be responsible for conducting experiments and simulations to validate RL strategies.

Requirements

• Experience with Q-Learning
• Experience with Deep Q Networks (DQN)
• Experience with Policy Gradient Methods
• Experience with Contextual Bandits
• Experience with Markov Decision Processes (MDP)
• Experience with sequential and behavioral modeling
• Experience with stochastic modeling and probabilistic methods
• Experience with machine learning pipelines in Databricks or similar distributed computing environments
• Experience with experimentation, simulation, and offline policy evaluation

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, and 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed