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Data Scientist – Reinforcement Learning
EXLData Scientist developing Reinforcement Learning models for Collections strategy initiatives. Building intelligent decisioning systems and optimizing customer treatment paths.
Tech Stack
Tools & technologiesAWSAzureCloudGoogle Cloud PlatformPyTorchTensorflow
About the role
Key responsibilities & impact- 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.
Requirements
What you’ll need- 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
Benefits
Comp & perks- 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)
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Reinforcement LearningQ-LearningDeep Q NetworksPolicy Gradient MethodsContextual BanditsMarkov Decision Processesstochastic modelingprobabilistic methodsmachine learning pipelinescustomer engagement modeling
Soft Skills
collaborationproblem-solvingcommunication