Senior Data Scientist - Reinforcement Learning

EXL•United States,

About The Position

This role focuses on designing and developing Reinforcement Learning (RL) models to optimize various aspects of collections and customer treatment. The Senior Data Scientist will build adaptive decisioning systems, develop sequential and behavioral models, and apply stochastic modeling techniques. Collaboration with business stakeholders, engineering, and MLOps teams is crucial for translating problems into AI/ML solutions, building pipelines, conducting experiments, and deploying/monitoring models. The position also involves mentoring junior data scientists and promoting best practices.

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 stochastic modeling and probabilistic methods
Experience building and maintaining machine learning pipelines in Databricks or similar distributed computing environments
Experience with experimentation, simulation, and offline policy evaluation
Experience working with large-scale structured and unstructured datasets
Experience partnering with engineering and MLOps teams
Experience mentoring junior data scientists

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.
Work with large-scale structured and unstructured datasets to derive actionable insights and improve operational performance.
Partner with engineering and MLOps teams to deploy and monitor production-grade ML/RL models.
Mentor junior data scientists and promote best practices in modeling, experimentation, and AI governance.