Senior Data Scientist - Reinforcement Learning

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. 
• 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. 
• Hands-on expertise with: 
  • Reinforcement Learning algorithms (Q-Learning, DQN, PPO, Bandits, etc.) 
  • Markov Decision Processes (MDP) 
  • Stochastic modeling and probabilistic systems 
  • Machine learning and predictive modeling 
  • 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. 
• Strong understanding of feature engineering, model validation, and performance optimization. 
• Ability to communicate complex AI/ML concepts to technical and non-technical stakeholders. 

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 
• Exposure to causal inference, uplift modeling, or optimization techniques. 
• Knowledge of customer lifecycle analytics and behavioral segmentation. 
• Experience working in Agile delivery environments.

• 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) 
  • Stochastic modeling and probabilistic systems 
  • Machine learning and predictive modeling 
  • Experimentation and simulation frameworks