Data Scientist - Reinforcement Learning

Data Science-Data Scientist

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:

• 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.

Preferred / Good-to-Have Skill

• 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

Must-Have Qualifications

• 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)

Required Skills

• Adapting To Change
• Attention To Consistency
• Big Data Optimization
• Cloud Security Management
• Data Backup and Recovery
• Data Strategy
• Interpersonal Dynamics with Coworkers
• Results Orientation
• Stakeholder Engagement
• Time Management Skills
• Working under Pressure
• Writing Communication Skills