Senior Data Scientist - Reinforcement Learning

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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