About the Role:

EXL is seeking 2 highly skilled Data Scientists with expertise in Reinforcement Learning (RL), optimization, and advanced analytics to support Collections strategy initiatives for a major client. This role focuses on developing intelligent decisioning systems and adaptive collection strategies using reinforcement learning, sequential modeling, and machine learning techniques.

**This role is hybrid in Philadelphia, PA but we will also accept candidates based out of NY/NJ.

Key Responsibilities:

• Design and develop Reinforcement Learning models to optimize collections strategies, customer treatment paths, and recovery outcomes.
• 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.

Must-Have Qualifications:

• Strong experience in Reinforcement Learning and sequential decision-making systems, using algorithms such as Q-Learning, DQN, PPO, Bandits, etc.
• Experimentation and simulation frameworks.
• Strong programming skills in Python and SQL.
• Experience with Databricks, Spark, or similar big data/cloud analytics platforms.
• Experience building scalable ML pipelines and deploying models into production environments.
• Ability to communicate complex AI/ML concepts to technical and non-technical stakeholders.

Preferred Skills:

• Experience in collections, credit risk, customer analytics, or financial services domains.
• Familiarity with deep Learning frameworks such as TensorFlow and PyTorch
• MLOps and CI/CD workflows
• Cloud platforms such as AWS, Azure, or GCP
• Exposure to causal inference, uplift modeling, or optimization techniques.
• Knowledge of customer lifecycle analytics and behavioral segmentation.
• Experience working in Agile delivery environments.

Pay range and compensation package: Details regarding pay range or salary or compensation will be provided during the interview process.

Equal Opportunity Statement: We are committed to diversity and inclusivity in our hiring practices.