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Data Scientist - Reinforcement Learning

EXL Service -
United States

Job Details

Full-time

Qualifications

Reinforcement learning

Full Job Description

Job Description: 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.

Responsibilities: 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

Qualifications: 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)

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