Data Scientist – Reinforcement Learning

• Role: Data Scientist – Reinforcement Learning
• Location: Philadelphia/NY/NJ -Hybrid
• Hire Type: Full-Time Permanent Role(No Contract)

Role Overview:

• We are seeking a highly skilled Data Scientist with expertise in Reinforcement Learning (RL), optimization, and advanced analytics.
• This role focuses on developing intelligent decisioning systems and adaptive strategies using reinforcement learning, sequential modeling, and machine learning techniques. The ideal candidate should have strong experience in building AI-driven systems that optimize customer engagement, payment recovery, treatment allocation, and workflow optimization across large-scale environments.
• The role requires a strong foundation in machine learning, stochastic processes, experimentation, and scalable model deployment using modern cloud and big data platforms.
• Key Responsibilities:
• Design and develop Reinforcement Learning models to optimize 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 prioritization strategies.
• Apply stochastic modeling and probabilistic methods to optimize dynamic treatment strategies under uncertainty.
• Collaborate with business stakeholders to translate complex business 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.