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Senior Data Scientist – Reinforcement Learning
EXLSenior Data Scientist focusing on Reinforcement Learning and advanced analytics for Collections strategy initiatives. Developing intelligent decisioning systems and adaptive strategies for major client.
ATS Keywords
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Hard Skills
Reinforcement LearningQ-LearningDeep Q NetworksPolicy Gradient MethodsContextual BanditsMarkov Decision Processesstochastic modelingprobabilistic methodsmachine learningpredictive modeling
Soft Skills
communicationmentoringcollaborationproblem-solving
Tools & Technologies
DatabricksSparkbig datacloud analyticsMLOps
Industry Keywords
collections strategiescustomer treatment pathsdynamic treatment strategiesAI governancemodel validationperformance optimization
Tech Stack
Tools & technologiesCloudPythonSparkSQL
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.
- 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.
Requirements
What you’ll need- 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.
Benefits
Comp & perks- Health insurance
- Flexible working arrangements
- Professional development opportunities